[inductor] use triu ref instead of lowering (#96040) (#96462)

Fixes #95958
Generated code is functionally identical with ref and lowering, only minor differences

Pull Request resolved: https://github.com/pytorch/pytorch/pull/96040
Approved by: https://github.com/jansel

Co-authored-by: Natalia Gimelshein <ngimel@fb.com>

.bazelignore

@@ -1,4 +0,0 @@
-# We do not use this library in our Bazel build. It contains an
-# infinitely recursing symlink that makes Bazel very unhappy.
-third_party/ittapi/
-third_party/opentelemetry-cpp

.bazelrc

@@ -69,6 +69,10 @@ build --per_file_copt='^//.*\.(cpp|cc)$'@-Werror=all
 # The following warnings come from -Wall. We downgrade them from error
 # to warnings here.
 #
+# sign-compare has a tremendous amount of violations in the
+# codebase. It will be a lot of work to fix them, just disable it for
+# now.
+build --per_file_copt='^//.*\.(cpp|cc)$'@-Wno-sign-compare
 # We intentionally use #pragma unroll, which is compiler specific.
 build --per_file_copt='^//.*\.(cpp|cc)$'@-Wno-error=unknown-pragmas
 
@@ -96,9 +100,6 @@ build --per_file_copt='^//.*\.(cpp|cc)$'@-Wno-unused-parameter
 # likely want to have this disabled for the most part.
 build --per_file_copt='^//.*\.(cpp|cc)$'@-Wno-missing-field-initializers
 
-build --per_file_copt='^//.*\.(cpp|cc)$'@-Wno-unused-function
-build --per_file_copt='^//.*\.(cpp|cc)$'@-Wno-unused-variable
-
 build --per_file_copt='//:aten/src/ATen/RegisterCompositeExplicitAutograd\.cpp$'@-Wno-error=unused-function
 build --per_file_copt='//:aten/src/ATen/RegisterCompositeImplicitAutograd\.cpp$'@-Wno-error=unused-function
 build --per_file_copt='//:aten/src/ATen/RegisterMkldnnCPU\.cpp$'@-Wno-error=unused-function

.bazelversion

@@ -1 +1 @@
-6.1.1
+4.2.1

.buckconfig.oss

@@ -14,7 +14,6 @@
 
 [cxx]
   cxxflags = -std=c++17
-  ldflags = -Wl,--no-undefined
   should_remap_host_platform = true
   cpp = /usr/bin/clang
   cc = /usr/bin/clang

.ci/docker/README.md

@@ -1,7 +1,7 @@
-# Docker images for GitHub CI
+# Docker images for Jenkins
 
 This directory contains everything needed to build the Docker images
-that are used in our CI.
+that are used in our CI
 
 The Dockerfiles located in subdirectories are parameterized to
 conditionally run build stages depending on build arguments passed to
@@ -12,14 +12,13 @@ each image as the `BUILD_ENVIRONMENT` environment variable.
 
 See `build.sh` for valid build environments (it's the giant switch).
 
+Docker builds are now defined with `.circleci/cimodel/data/simple/docker_definitions.py`
+
 ## Contents
 
 * `build.sh` -- dispatch script to launch all builds
 * `common` -- scripts used to execute individual Docker build stages
-* `ubuntu` -- Dockerfile for Ubuntu image for CPU build and test jobs
 * `ubuntu-cuda` -- Dockerfile for Ubuntu image with CUDA support for nvidia-docker
-* `ubuntu-rocm` -- Dockerfile for Ubuntu image with ROCm support
-* `ubuntu-xpu` -- Dockerfile for Ubuntu image with XPU support
 
 ## Usage
 

.ci/docker/android/build.gradle

@@ -53,7 +53,7 @@ dependencies {
     implementation 'androidx.appcompat:appcompat:1.0.0'
     implementation 'com.facebook.fbjni:fbjni-java-only:0.2.2'
     implementation 'com.google.code.findbugs:jsr305:3.0.1'
-    implementation 'com.facebook.soloader:nativeloader:0.10.5'
+    implementation 'com.facebook.soloader:nativeloader:0.10.4'
 
     implementation 'junit:junit:' + rootProject.junitVersion
     implementation 'androidx.test:core:' + rootProject.coreVersion

.ci/docker/aotriton_version.txt

@@ -1,5 +0,0 @@
-0.6b
-manylinux_2_17
-rocm6
-04b5df8c8123f90cba3ede7e971e6fbc6040d506
-3db6ecbc915893ff967abd6e1b43bd5f54949868873be60dc802086c3863e648

.ci/docker/build.sh

@@ -46,7 +46,9 @@ if [[ "$image" == *xla* ]]; then
   exit 0
 fi
 
-if [[ "$image" == *-focal* ]]; then
+if [[ "$image" == *-bionic* ]]; then
+  UBUNTU_VERSION=18.04
+elif [[ "$image" == *-focal* ]]; then
   UBUNTU_VERSION=20.04
 elif [[ "$image" == *-jammy* ]]; then
   UBUNTU_VERSION=22.04
@@ -71,11 +73,6 @@ if [[ "$image" == *cuda* && "$UBUNTU_VERSION" != "22.04" ]]; then
   DOCKERFILE="${OS}-cuda/Dockerfile"
 elif [[ "$image" == *rocm* ]]; then
   DOCKERFILE="${OS}-rocm/Dockerfile"
-elif [[ "$image" == *xpu* ]]; then
-  DOCKERFILE="${OS}-xpu/Dockerfile"
-elif [[ "$image" == *cuda*linter* ]]; then
-  # Use a separate Dockerfile for linter to keep a small image size
-  DOCKERFILE="linter-cuda/Dockerfile"
 elif [[ "$image" == *linter* ]]; then
   # Use a separate Dockerfile for linter to keep a small image size
   DOCKERFILE="linter/Dockerfile"
@@ -84,18 +81,18 @@ fi
 # CMake 3.18 is needed to support CUDA17 language variant
 CMAKE_VERSION=3.18.5
 
-_UCX_COMMIT=7bb2722ff2187a0cad557ae4a6afa090569f83fb
-_UCC_COMMIT=20eae37090a4ce1b32bcce6144ccad0b49943e0b
+_UCX_COMMIT=31e74cac7bee0ef66bef2af72e7d86d9c282e5ab
+_UCC_COMMIT=1c7a7127186e7836f73aafbd7697bbc274a77eee
 
 # It's annoying to rename jobs every time you want to rewrite a
 # configuration, so we hardcode everything here rather than do it
 # from scratch
 case "$image" in
-  pytorch-linux-focal-cuda12.4-cudnn9-py3-gcc9)
-    CUDA_VERSION=12.4.0
-    CUDNN_VERSION=9
+  pytorch-linux-bionic-cuda11.6-cudnn8-py3-gcc7)
+    CUDA_VERSION=11.6.2
+    CUDNN_VERSION=8
     ANACONDA_PYTHON_VERSION=3.10
-    GCC_VERSION=9
+    GCC_VERSION=7
     PROTOBUF=yes
     DB=yes
     VISION=yes
@@ -103,13 +100,12 @@ case "$image" in
     UCX_COMMIT=${_UCX_COMMIT}
     UCC_COMMIT=${_UCC_COMMIT}
     CONDA_CMAKE=yes
-    TRITON=yes
     ;;
-  pytorch-linux-focal-cuda12.1-cudnn9-py3-gcc9)
-    CUDA_VERSION=12.1.1
-    CUDNN_VERSION=9
+  pytorch-linux-bionic-cuda11.7-cudnn8-py3-gcc7)
+    CUDA_VERSION=11.7.0
+    CUDNN_VERSION=8
     ANACONDA_PYTHON_VERSION=3.10
-    GCC_VERSION=9
+    GCC_VERSION=7
     PROTOBUF=yes
     DB=yes
     VISION=yes
@@ -117,73 +113,12 @@ case "$image" in
     UCX_COMMIT=${_UCX_COMMIT}
     UCC_COMMIT=${_UCC_COMMIT}
     CONDA_CMAKE=yes
-    TRITON=yes
     ;;
-  pytorch-linux-focal-cuda12.4-cudnn9-py3-gcc9-inductor-benchmarks)
-    CUDA_VERSION=12.4.0
-    CUDNN_VERSION=9
-    ANACONDA_PYTHON_VERSION=3.10
-    GCC_VERSION=9
-    PROTOBUF=yes
-    DB=yes
-    VISION=yes
-    KATEX=yes
-    UCX_COMMIT=${_UCX_COMMIT}
-    UCC_COMMIT=${_UCC_COMMIT}
-    CONDA_CMAKE=yes
-    TRITON=yes
-    INDUCTOR_BENCHMARKS=yes
-    ;;
-  pytorch-linux-focal-cuda12.1-cudnn9-py3-gcc9-inductor-benchmarks)
-    CUDA_VERSION=12.1.1
-    CUDNN_VERSION=9
-    ANACONDA_PYTHON_VERSION=3.10
-    GCC_VERSION=9
-    PROTOBUF=yes
-    DB=yes
-    VISION=yes
-    KATEX=yes
-    UCX_COMMIT=${_UCX_COMMIT}
-    UCC_COMMIT=${_UCC_COMMIT}
-    CONDA_CMAKE=yes
-    TRITON=yes
-    INDUCTOR_BENCHMARKS=yes
-    ;;
-  pytorch-linux-focal-cuda12.1-cudnn9-py3.12-gcc9-inductor-benchmarks)
-    CUDA_VERSION=12.1.1
-    CUDNN_VERSION=9
-    ANACONDA_PYTHON_VERSION=3.12
-    GCC_VERSION=9
-    PROTOBUF=yes
-    DB=yes
-    VISION=yes
-    KATEX=yes
-    UCX_COMMIT=${_UCX_COMMIT}
-    UCC_COMMIT=${_UCC_COMMIT}
-    CONDA_CMAKE=yes
-    TRITON=yes
-    INDUCTOR_BENCHMARKS=yes
-    ;;
-  pytorch-linux-focal-cuda12.4-cudnn9-py3.12-gcc9-inductor-benchmarks)
-    CUDA_VERSION=12.4.0
-    CUDNN_VERSION=9
-    ANACONDA_PYTHON_VERSION=3.12
-    GCC_VERSION=9
-    PROTOBUF=yes
-    DB=yes
-    VISION=yes
-    KATEX=yes
-    UCX_COMMIT=${_UCX_COMMIT}
-    UCC_COMMIT=${_UCC_COMMIT}
-    CONDA_CMAKE=yes
-    TRITON=yes
-    INDUCTOR_BENCHMARKS=yes
-    ;;
-  pytorch-linux-focal-cuda11.8-cudnn9-py3-gcc9)
+  pytorch-linux-bionic-cuda11.8-cudnn8-py3-gcc7)
     CUDA_VERSION=11.8.0
-    CUDNN_VERSION=9
+    CUDNN_VERSION=8
     ANACONDA_PYTHON_VERSION=3.10
-    GCC_VERSION=9
+    GCC_VERSION=7
     PROTOBUF=yes
     DB=yes
     VISION=yes
@@ -191,49 +126,14 @@ case "$image" in
     UCX_COMMIT=${_UCX_COMMIT}
     UCC_COMMIT=${_UCC_COMMIT}
     CONDA_CMAKE=yes
-    TRITON=yes
     ;;
-  pytorch-linux-focal-cuda12.4-cudnn9-py3-gcc9)
-    CUDA_VERSION=12.4.0
-    CUDNN_VERSION=9
-    ANACONDA_PYTHON_VERSION=3.10
-    GCC_VERSION=9
+  pytorch-linux-focal-py3-clang7-asan)
+    ANACONDA_PYTHON_VERSION=3.9
+    CLANG_VERSION=7
     PROTOBUF=yes
     DB=yes
     VISION=yes
-    KATEX=yes
-    UCX_COMMIT=${_UCX_COMMIT}
-    UCC_COMMIT=${_UCC_COMMIT}
     CONDA_CMAKE=yes
-    TRITON=yes
-    ;;
-  pytorch-linux-focal-cuda12.1-cudnn9-py3-gcc9)
-    CUDA_VERSION=12.1.1
-    CUDNN_VERSION=9
-    ANACONDA_PYTHON_VERSION=3.10
-    GCC_VERSION=9
-    PROTOBUF=yes
-    DB=yes
-    VISION=yes
-    KATEX=yes
-    UCX_COMMIT=${_UCX_COMMIT}
-    UCC_COMMIT=${_UCC_COMMIT}
-    CONDA_CMAKE=yes
-    TRITON=yes
-    ;;
-  pytorch-linux-focal-cuda12.4-cudnn9-py3-gcc9)
-    CUDA_VERSION=12.4.0
-    CUDNN_VERSION=9
-    ANACONDA_PYTHON_VERSION=3.10
-    GCC_VERSION=9
-    PROTOBUF=yes
-    DB=yes
-    VISION=yes
-    KATEX=yes
-    UCX_COMMIT=${_UCX_COMMIT}
-    UCC_COMMIT=${_UCC_COMMIT}
-    CONDA_CMAKE=yes
-    TRITON=yes
     ;;
   pytorch-linux-focal-py3-clang10-onnx)
     ANACONDA_PYTHON_VERSION=3.8
@@ -242,48 +142,44 @@ case "$image" in
     DB=yes
     VISION=yes
     CONDA_CMAKE=yes
-    ONNX=yes
     ;;
-  pytorch-linux-focal-py3-clang9-android-ndk-r21e)
-    ANACONDA_PYTHON_VERSION=3.8
-    CLANG_VERSION=9
+  pytorch-linux-focal-py3-clang7-android-ndk-r19c)
+    ANACONDA_PYTHON_VERSION=3.7
+    CLANG_VERSION=7
     LLVMDEV=yes
     PROTOBUF=yes
     ANDROID=yes
-    ANDROID_NDK_VERSION=r21e
+    ANDROID_NDK_VERSION=r19c
     GRADLE_VERSION=6.8.3
     NINJA_VERSION=1.9.0
     ;;
-  pytorch-linux-focal-py3.8-clang10)
+  pytorch-linux-bionic-py3.8-clang9)
     ANACONDA_PYTHON_VERSION=3.8
-    CLANG_VERSION=10
+    CLANG_VERSION=9
     PROTOBUF=yes
     DB=yes
     VISION=yes
     VULKAN_SDK_VERSION=1.2.162.1
     SWIFTSHADER=yes
     CONDA_CMAKE=yes
-    TRITON=yes
     ;;
-  pytorch-linux-focal-py3.11-clang10)
+  pytorch-linux-bionic-py3.11-clang9)
     ANACONDA_PYTHON_VERSION=3.11
-    CLANG_VERSION=10
+    CLANG_VERSION=9
     PROTOBUF=yes
     DB=yes
     VISION=yes
     VULKAN_SDK_VERSION=1.2.162.1
     SWIFTSHADER=yes
     CONDA_CMAKE=yes
-    TRITON=yes
     ;;
-  pytorch-linux-focal-py3.8-gcc9)
+  pytorch-linux-bionic-py3.8-gcc9)
     ANACONDA_PYTHON_VERSION=3.8
     GCC_VERSION=9
     PROTOBUF=yes
     DB=yes
     VISION=yes
     CONDA_CMAKE=yes
-    TRITON=yes
     ;;
   pytorch-linux-focal-rocm-n-1-py3)
     ANACONDA_PYTHON_VERSION=3.8
@@ -291,10 +187,9 @@ case "$image" in
     PROTOBUF=yes
     DB=yes
     VISION=yes
-    ROCM_VERSION=6.0
+    ROCM_VERSION=5.3
     NINJA_VERSION=1.9.0
     CONDA_CMAKE=yes
-    TRITON=yes
     ;;
   pytorch-linux-focal-rocm-n-py3)
     ANACONDA_PYTHON_VERSION=3.8
@@ -302,76 +197,45 @@ case "$image" in
     PROTOBUF=yes
     DB=yes
     VISION=yes
-    ROCM_VERSION=6.1
+    ROCM_VERSION=5.4.2
     NINJA_VERSION=1.9.0
     CONDA_CMAKE=yes
-    TRITON=yes
     ;;
-  pytorch-linux-jammy-xpu-2024.0-py3)
+  pytorch-linux-focal-py3.8-gcc7)
     ANACONDA_PYTHON_VERSION=3.8
-    GCC_VERSION=11
-    PROTOBUF=yes
-    DB=yes
-    VISION=yes
-    XPU_VERSION=0.5
-    NINJA_VERSION=1.9.0
-    CONDA_CMAKE=yes
-    TRITON=yes
-    ;;
-    pytorch-linux-jammy-py3.8-gcc11-inductor-benchmarks)
-    ANACONDA_PYTHON_VERSION=3.8
-    GCC_VERSION=11
+    GCC_VERSION=7
     PROTOBUF=yes
     DB=yes
     VISION=yes
     KATEX=yes
     CONDA_CMAKE=yes
-    TRITON=yes
-    DOCS=yes
-    INDUCTOR_BENCHMARKS=yes
     ;;
-  pytorch-linux-jammy-cuda11.8-cudnn9-py3.8-clang12)
+  pytorch-linux-jammy-cuda11.6-cudnn8-py3.8-clang12)
+    ANACONDA_PYTHON_VERSION=3.8
+    CUDA_VERSION=11.6
+    CUDNN_VERSION=8
+    CLANG_VERSION=12
+    PROTOBUF=yes
+    DB=yes
+    VISION=yes
+    ;;
+  pytorch-linux-jammy-cuda11.7-cudnn8-py3.8-clang12)
+    ANACONDA_PYTHON_VERSION=3.8
+    CUDA_VERSION=11.7
+    CUDNN_VERSION=8
+    CLANG_VERSION=12
+    PROTOBUF=yes
+    DB=yes
+    VISION=yes
+    ;;
+  pytorch-linux-jammy-cuda11.8-cudnn8-py3.8-clang12)
     ANACONDA_PYTHON_VERSION=3.8
     CUDA_VERSION=11.8
-    CUDNN_VERSION=9
+    CUDNN_VERSION=8
     CLANG_VERSION=12
     PROTOBUF=yes
     DB=yes
     VISION=yes
-    TRITON=yes
-    ;;
-  pytorch-linux-jammy-py3-clang12-asan)
-    ANACONDA_PYTHON_VERSION=3.9
-    CLANG_VERSION=12
-    PROTOBUF=yes
-    DB=yes
-    VISION=yes
-    CONDA_CMAKE=yes
-    TRITON=yes
-    ;;
-  pytorch-linux-jammy-py3-clang15-asan)
-    ANACONDA_PYTHON_VERSION=3.10
-    CLANG_VERSION=15
-    CONDA_CMAKE=yes
-    VISION=yes
-    ;;
-  pytorch-linux-jammy-py3.8-gcc11)
-    ANACONDA_PYTHON_VERSION=3.8
-    GCC_VERSION=11
-    PROTOBUF=yes
-    DB=yes
-    VISION=yes
-    KATEX=yes
-    CONDA_CMAKE=yes
-    TRITON=yes
-    DOCS=yes
-    UNINSTALL_DILL=yes
-    ;;
-  pytorch-linux-jammy-py3-clang12-executorch)
-    ANACONDA_PYTHON_VERSION=3.10
-    CLANG_VERSION=12
-    CONDA_CMAKE=yes
-    EXECUTORCH=yes
     ;;
   pytorch-linux-focal-linter)
     # TODO: Use 3.9 here because of this issue https://github.com/python/mypy/issues/13627.
@@ -380,26 +244,6 @@ case "$image" in
     ANACONDA_PYTHON_VERSION=3.9
     CONDA_CMAKE=yes
     ;;
-  pytorch-linux-jammy-cuda11.8-cudnn9-py3.9-linter)
-    ANACONDA_PYTHON_VERSION=3.9
-    CUDA_VERSION=11.8
-    CONDA_CMAKE=yes
-    ;;
-  pytorch-linux-jammy-aarch64-py3.10-gcc11)
-    ANACONDA_PYTHON_VERSION=3.10
-    GCC_VERSION=11
-    ACL=yes
-    PROTOBUF=yes
-    DB=yes
-    VISION=yes
-    CONDA_CMAKE=yes
-    # snadampal: skipping sccache due to the following issue
-    # https://github.com/pytorch/pytorch/issues/121559
-    SKIP_SCCACHE_INSTALL=yes
-    # snadampal: skipping llvm src build install because the current version
-    # from pytorch/llvm:9.0.1 is x86 specific
-    SKIP_LLVM_SRC_BUILD_INSTALL=yes
-    ;;
   *)
     # Catch-all for builds that are not hardcoded.
     PROTOBUF=yes
@@ -416,10 +260,6 @@ case "$image" in
     if [[ "$image" == *rocm* ]]; then
       extract_version_from_image_name rocm ROCM_VERSION
       NINJA_VERSION=1.9.0
-      TRITON=yes
-      # To ensure that any ROCm config will build using conda cmake
-      # and thus have LAPACK/MKL enabled
-      CONDA_CMAKE=yes
     fi
     if [[ "$image" == *centos7* ]]; then
       NINJA_VERSION=1.10.2
@@ -447,17 +287,20 @@ tmp_tag=$(basename "$(mktemp -u)" | tr '[:upper:]' '[:lower:]')
 #when using cudnn version 8 install it separately from cuda
 if [[ "$image" == *cuda*  && ${OS} == "ubuntu" ]]; then
   IMAGE_NAME="nvidia/cuda:${CUDA_VERSION}-cudnn${CUDNN_VERSION}-devel-ubuntu${UBUNTU_VERSION}"
-  if [[ ${CUDNN_VERSION} == 9 ]]; then
+  if [[ ${CUDNN_VERSION} == 8 ]]; then
     IMAGE_NAME="nvidia/cuda:${CUDA_VERSION}-devel-ubuntu${UBUNTU_VERSION}"
   fi
 fi
 
 # Build image
+# TODO: build-arg THRIFT is not turned on for any image, remove it once we confirm
+# it's no longer needed.
 docker build \
        --no-cache \
        --progress=plain \
        --build-arg "BUILD_ENVIRONMENT=${image}" \
        --build-arg "PROTOBUF=${PROTOBUF:-}" \
+       --build-arg "THRIFT=${THRIFT:-}" \
        --build-arg "LLVMDEV=${LLVMDEV:-}" \
        --build-arg "DB=${DB:-}" \
        --build-arg "VISION=${VISION:-}" \
@@ -480,26 +323,17 @@ docker build \
        --build-arg "NINJA_VERSION=${NINJA_VERSION:-}" \
        --build-arg "KATEX=${KATEX:-}" \
        --build-arg "ROCM_VERSION=${ROCM_VERSION:-}" \
-       --build-arg "PYTORCH_ROCM_ARCH=${PYTORCH_ROCM_ARCH:-gfx906;gfx90a}" \
+       --build-arg "PYTORCH_ROCM_ARCH=${PYTORCH_ROCM_ARCH:-gfx906}" \
        --build-arg "IMAGE_NAME=${IMAGE_NAME}" \
        --build-arg "UCX_COMMIT=${UCX_COMMIT}" \
        --build-arg "UCC_COMMIT=${UCC_COMMIT}" \
        --build-arg "CONDA_CMAKE=${CONDA_CMAKE}" \
-       --build-arg "TRITON=${TRITON}" \
-       --build-arg "ONNX=${ONNX}" \
-       --build-arg "DOCS=${DOCS}" \
-       --build-arg "INDUCTOR_BENCHMARKS=${INDUCTOR_BENCHMARKS}" \
-       --build-arg "EXECUTORCH=${EXECUTORCH}" \
-       --build-arg "XPU_VERSION=${XPU_VERSION}" \
-       --build-arg "ACL=${ACL:-}" \
-       --build-arg "SKIP_SCCACHE_INSTALL=${SKIP_SCCACHE_INSTALL:-}" \
-       --build-arg "SKIP_LLVM_SRC_BUILD_INSTALL=${SKIP_LLVM_SRC_BUILD_INSTALL:-}" \
        -f $(dirname ${DOCKERFILE})/Dockerfile \
        -t "$tmp_tag" \
        "$@" \
        .
 
-# NVIDIA dockers for RC releases use tag names like `11.0-cudnn9-devel-ubuntu18.04-rc`,
+# NVIDIA dockers for RC releases use tag names like `11.0-cudnn8-devel-ubuntu18.04-rc`,
 # for this case we will set UBUNTU_VERSION to `18.04-rc` so that the Dockerfile could
 # find the correct image. As a result, here we have to replace the
 #   "$UBUNTU_VERSION" == "18.04-rc"

.ci/docker/build_docker.sh

@@ -0,0 +1,60 @@
+#!/bin/bash
+
+set -ex
+
+retry () {
+    $*  || (sleep 1 && $*) || (sleep 2 && $*)
+}
+
+# If UPSTREAM_BUILD_ID is set (see trigger job), then we can
+# use it to tag this build with the same ID used to tag all other
+# base image builds. Also, we can try and pull the previous
+# image first, to avoid rebuilding layers that haven't changed.
+
+#until we find a way to reliably reuse previous build, this last_tag is not in use
+# last_tag="$(( CIRCLE_BUILD_NUM - 1 ))"
+tag="${DOCKER_TAG}"
+
+
+registry="308535385114.dkr.ecr.us-east-1.amazonaws.com"
+image="${registry}/pytorch/${IMAGE_NAME}"
+
+login() {
+  aws ecr get-authorization-token --region us-east-1 --output text --query 'authorizationData[].authorizationToken' |
+    base64 -d |
+    cut -d: -f2 |
+    docker login -u AWS --password-stdin "$1"
+}
+
+
+# Only run these steps if not on github actions
+if [[ -z "${GITHUB_ACTIONS}" ]]; then
+  # Retry on timeouts (can happen on job stampede).
+  retry login "${registry}"
+  # Logout on exit
+  trap "docker logout ${registry}" EXIT
+fi
+
+# Try to pull the previous image (perhaps we can reuse some layers)
+# if [ -n "${last_tag}" ]; then
+#   docker pull "${image}:${last_tag}" || true
+# fi
+
+# Build new image
+./build.sh ${IMAGE_NAME} -t "${image}:${tag}"
+
+# Only push if `DOCKER_SKIP_PUSH` = false
+if [ "${DOCKER_SKIP_PUSH:-true}" = "false" ]; then
+  # Only push if docker image doesn't exist already.
+  # ECR image tags are immutable so this will avoid pushing if only just testing if the docker jobs work
+  # NOTE: The only workflow that should push these images should be the docker-builds.yml workflow
+  if ! docker manifest inspect "${image}:${tag}" >/dev/null 2>/dev/null; then
+    docker push "${image}:${tag}"
+  fi
+fi
+
+if [ -z "${DOCKER_SKIP_S3_UPLOAD:-}" ]; then
+  trap "rm -rf ${IMAGE_NAME}:${tag}.tar" EXIT
+  docker save -o "${IMAGE_NAME}:${tag}.tar" "${image}:${tag}"
+  aws s3 cp "${IMAGE_NAME}:${tag}.tar" "s3://ossci-linux-build/pytorch/base/${IMAGE_NAME}:${tag}.tar" --acl public-read
+fi

.ci/docker/centos-rocm/Dockerfile

@@ -62,11 +62,11 @@ RUN if [ -n "${DB}" ]; then bash ./install_db.sh; fi
 RUN rm install_db.sh
 ENV INSTALLED_DB ${DB}
 
-# (optional) Install vision packages like OpenCV
+# (optional) Install vision packages like OpenCV and ffmpeg
 ARG VISION
-COPY ./common/install_vision.sh ./common/cache_vision_models.sh ./common/common_utils.sh ./
+COPY ./common/install_vision.sh install_vision.sh
 RUN if [ -n "${VISION}" ]; then bash ./install_vision.sh; fi
-RUN rm install_vision.sh cache_vision_models.sh common_utils.sh
+RUN rm install_vision.sh
 ENV INSTALLED_VISION ${VISION}
 
 # Install rocm
@@ -77,9 +77,6 @@ RUN rm install_rocm.sh
 COPY ./common/install_rocm_magma.sh install_rocm_magma.sh
 RUN bash ./install_rocm_magma.sh
 RUN rm install_rocm_magma.sh
-COPY ./common/install_amdsmi.sh install_amdsmi.sh
-RUN bash ./install_amdsmi.sh
-RUN rm install_amdsmi.sh
 ENV PATH /opt/rocm/bin:$PATH
 ENV PATH /opt/rocm/hcc/bin:$PATH
 ENV PATH /opt/rocm/hip/bin:$PATH
@@ -101,25 +98,6 @@ COPY ./common/install_ninja.sh install_ninja.sh
 RUN if [ -n "${NINJA_VERSION}" ]; then bash ./install_ninja.sh; fi
 RUN rm install_ninja.sh
 
-ARG TRITON
-# Install triton, this needs to be done before sccache because the latter will
-# try to reach out to S3, which docker build runners don't have access
-ENV CMAKE_C_COMPILER cc
-ENV CMAKE_CXX_COMPILER c++
-COPY ./common/install_triton.sh install_triton.sh
-COPY ./common/common_utils.sh common_utils.sh
-COPY ci_commit_pins/triton-rocm.txt triton-rocm.txt
-COPY triton_version.txt triton_version.txt
-RUN if [ -n "${TRITON}" ]; then bash ./install_triton.sh; fi
-RUN rm install_triton.sh common_utils.sh triton-rocm.txt triton_version.txt
-
-# Install AOTriton (Early fail)
-COPY ./aotriton_version.txt aotriton_version.txt
-COPY ./common/common_utils.sh common_utils.sh
-COPY ./common/install_aotriton.sh install_aotriton.sh
-RUN ["/bin/bash", "-c", "./install_aotriton.sh /opt/rocm && rm -rf install_aotriton.sh aotriton_version.txt common_utils.sh"]
-ENV AOTRITON_INSTALLED_PREFIX /opt/rocm/aotriton
-
 # Install ccache/sccache (do this last, so we get priority in PATH)
 COPY ./common/install_cache.sh install_cache.sh
 ENV PATH /opt/cache/bin:$PATH

.ci/docker/ci_commit_pins/executorch.txt

@@ -1 +0,0 @@
-d4b3e5cc607e97afdba79dc90f8ef968142f347c

.ci/docker/ci_commit_pins/huggingface.txt

@@ -1 +0,0 @@
-243e186efbf7fb93328dd6b34927a4e8c8f24395

.ci/docker/ci_commit_pins/timm.txt

@@ -1 +0,0 @@
-730b907b4d45a4713cbc425cbf224c46089fd514

.ci/docker/ci_commit_pins/triton-rocm.txt

@@ -1 +0,0 @@
-01cbe5045a6898c9a925f01435c8277b2fe6afcc

.ci/docker/ci_commit_pins/triton-xpu.txt

@@ -1 +0,0 @@
-b8c64f64c18d8cac598b3adb355c21e7439c21de

.ci/docker/ci_commit_pins/triton.txt

@@ -1 +0,0 @@
-45fff310c891f5a92d55445adf8cc9d29df5841e

.ci/docker/common/cache_vision_models.sh

@@ -1,18 +0,0 @@
-#!/bin/bash
-
-set -ex
-
-source "$(dirname "${BASH_SOURCE[0]}")/common_utils.sh"
-
-# Cache the test models at ~/.cache/torch/hub/
-IMPORT_SCRIPT_FILENAME="/tmp/torchvision_import_script.py"
-as_jenkins echo 'import torchvision; torchvision.models.mobilenet_v2(pretrained=True); torchvision.models.mobilenet_v3_large(pretrained=True);' > "${IMPORT_SCRIPT_FILENAME}"
-
-pip_install --pre torch torchvision --index-url https://download.pytorch.org/whl/nightly/cpu
-# Very weird quoting behavior here https://github.com/conda/conda/issues/10972,
-# so echo the command to a file and run the file instead
-conda_run python "${IMPORT_SCRIPT_FILENAME}"
-
-# Cleaning up
-conda_run pip uninstall -y torch torchvision
-rm "${IMPORT_SCRIPT_FILENAME}" || true

.ci/docker/common/common_utils.sh

@@ -13,7 +13,7 @@ as_jenkins() {
   # NB: Pass on PATH and LD_LIBRARY_PATH to sudo invocation
   # NB: This must be run from a directory that jenkins has access to,
   # works around https://github.com/conda/conda-package-handling/pull/34
-  $SUDO -E -H -u jenkins env -u SUDO_UID -u SUDO_GID -u SUDO_COMMAND -u SUDO_USER env "PATH=$PATH" "LD_LIBRARY_PATH=$LD_LIBRARY_PATH" $*
+  $SUDO -H -u jenkins env -u SUDO_UID -u SUDO_GID -u SUDO_COMMAND -u SUDO_USER env "PATH=$PATH" "LD_LIBRARY_PATH=$LD_LIBRARY_PATH" $*
 }
 
 conda_install() {
@@ -30,7 +30,3 @@ conda_run() {
 pip_install() {
   as_jenkins conda run -n py_$ANACONDA_PYTHON_VERSION pip install --progress-bar off $*
 }
-
-get_pinned_commit() {
-  cat "${1}".txt
-}

.ci/docker/common/install_acl.sh

@@ -1,16 +0,0 @@
-set -euo pipefail
-
-readonly version=v24.04
-readonly src_host=https://review.mlplatform.org/ml
-readonly src_repo=ComputeLibrary
-
-# Clone ACL
-[[ ! -d ${src_repo} ]] && git clone ${src_host}/${src_repo}.git
-cd ${src_repo}
-
-git checkout $version
-
-# Build with scons
-scons -j8  Werror=0 debug=0 neon=1 opencl=0 embed_kernels=0 \
-  os=linux arch=armv8a build=native multi_isa=1 \
-  fixed_format_kernels=1 openmp=1 cppthreads=0

.ci/docker/common/install_amdsmi.sh

@@ -1,5 +0,0 @@
-#!/bin/bash
-
-set -ex
-
-cd /opt/rocm/share/amd_smi && pip install .

.ci/docker/common/install_android.sh

@@ -107,6 +107,3 @@ chgrp -R jenkins /var/lib/jenkins/.gradle
 popd
 
 rm -rf /var/lib/jenkins/.gradle/daemon
-
-# Cache vision models used by the test
-source "$(dirname "${BASH_SOURCE[0]}")/cache_vision_models.sh"

.ci/docker/common/install_aotriton.sh

@@ -1,23 +0,0 @@
-#!/bin/bash
-
-set -ex
-
-source "$(dirname "${BASH_SOURCE[0]}")/common_utils.sh"
-
-TARBALL='aotriton.tar.bz2'
-# This read command alwasy returns with exit code 1
-read -d "\n" VER MANYLINUX ROCMBASE PINNED_COMMIT SHA256 < aotriton_version.txt || true
-ARCH=$(uname -m)
-AOTRITON_INSTALL_PREFIX="$1"
-AOTRITON_URL="https://github.com/ROCm/aotriton/releases/download/${VER}/aotriton-${VER}-${MANYLINUX}_${ARCH}-${ROCMBASE}.tar.bz2"
-
-cd "${AOTRITON_INSTALL_PREFIX}"
-# Must use -L to follow redirects
-curl -L --retry 3 -o "${TARBALL}" "${AOTRITON_URL}"
-ACTUAL_SHA256=$(sha256sum "${TARBALL}" | cut -d " " -f 1)
-if [ "${SHA256}" != "${ACTUAL_SHA256}" ]; then
-  echo -n "Error: The SHA256 of downloaded tarball is ${ACTUAL_SHA256},"
-  echo " which does not match the expected value ${SHA256}."
-  exit
-fi
-tar xf "${TARBALL}" && rm -rf "${TARBALL}"

.ci/docker/common/install_base.sh

@@ -3,13 +3,16 @@
 set -ex
 
 install_ubuntu() {
-  # NVIDIA dockers for RC releases use tag names like `11.0-cudnn9-devel-ubuntu18.04-rc`,
+  # NVIDIA dockers for RC releases use tag names like `11.0-cudnn8-devel-ubuntu18.04-rc`,
   # for this case we will set UBUNTU_VERSION to `18.04-rc` so that the Dockerfile could
   # find the correct image. As a result, here we have to check for
   #   "$UBUNTU_VERSION" == "18.04"*
   # instead of
   #   "$UBUNTU_VERSION" == "18.04"
-  if [[ "$UBUNTU_VERSION" == "20.04"* ]]; then
+  if [[ "$UBUNTU_VERSION" == "18.04"* ]]; then
+    cmake3="cmake=3.10*"
+    maybe_libiomp_dev="libiomp-dev"
+  elif [[ "$UBUNTU_VERSION" == "20.04"* ]]; then
     cmake3="cmake=3.16*"
     maybe_libiomp_dev=""
   elif [[ "$UBUNTU_VERSION" == "22.04"* ]]; then
@@ -20,9 +23,7 @@ install_ubuntu() {
     maybe_libiomp_dev="libiomp-dev"
   fi
 
-  if [[ "$CLANG_VERSION" == 15 ]]; then
-    maybe_libomp_dev="libomp-15-dev"
-  elif [[ "$CLANG_VERSION" == 12 ]]; then
+  if [[ "$CLANG_VERSION" == 12 ]]; then
     maybe_libomp_dev="libomp-12-dev"
   elif [[ "$CLANG_VERSION" == 10 ]]; then
     maybe_libomp_dev="libomp-10-dev"
@@ -30,13 +31,10 @@ install_ubuntu() {
     maybe_libomp_dev=""
   fi
 
-  # HACK: UCC testing relies on libnccl library from NVIDIA repo, and version 2.16 crashes
-  # See https://github.com/pytorch/pytorch/pull/105260#issuecomment-1673399729
-  if [[ "$UBUNTU_VERSION" == "20.04"* && "$CUDA_VERSION" == "11.8"* ]]; then
-    maybe_libnccl_dev="libnccl2=2.15.5-1+cuda11.8 libnccl-dev=2.15.5-1+cuda11.8 --allow-downgrades --allow-change-held-packages"
-  else
-    maybe_libnccl_dev=""
-  fi
+  # TODO: Remove this once nvidia package repos are back online
+  # Comment out nvidia repositories to prevent them from getting apt-get updated, see https://github.com/pytorch/pytorch/issues/74968
+  # shellcheck disable=SC2046
+  sed -i 's/.*nvidia.*/# &/' $(find /etc/apt/ -type f -name "*.list")
 
   # Install common dependencies
   apt-get update
@@ -61,12 +59,10 @@ install_ubuntu() {
     ${maybe_libiomp_dev} \
     libyaml-dev \
     libz-dev \
-    libjemalloc2 \
     libjpeg-dev \
     libasound2-dev \
     libsndfile-dev \
     ${maybe_libomp_dev} \
-    ${maybe_libnccl_dev} \
     software-properties-common \
     wget \
     sudo \
@@ -75,13 +71,26 @@ install_ubuntu() {
     libtool \
     vim \
     unzip \
-    gpg-agent \
     gdb
 
   # Should resolve issues related to various apt package repository cert issues
   # see: https://github.com/pytorch/pytorch/issues/65931
   apt-get install -y libgnutls30
 
+  # cuda-toolkit does not work with gcc-11.2.0 which is default in Ubunutu 22.04
+  # see: https://github.com/NVlabs/instant-ngp/issues/119
+  if [[ "$UBUNTU_VERSION" == "22.04"* ]]; then
+    apt-get install -y g++-10
+    update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-10 30
+    update-alternatives --install /usr/bin/g++ g++ /usr/bin/g++-10 30
+    update-alternatives --install /usr/bin/gcov gcov /usr/bin/gcov-10 30
+
+    # https://www.spinics.net/lists/libreoffice/msg07549.html
+    sudo rm -rf /usr/lib/gcc/x86_64-linux-gnu/11
+    wget https://github.com/gcc-mirror/gcc/commit/2b2d97fc545635a0f6aa9c9ee3b017394bc494bf.patch -O noexecpt.patch
+    sudo patch  /usr/include/c++/10/bits/range_access.h noexecpt.patch
+  fi
+
   # Cleanup package manager
   apt-get autoclean && apt-get clean
   rm -rf /var/lib/apt/lists/* /tmp/* /var/tmp/*
@@ -113,6 +122,7 @@ install_centos() {
     glibc-devel \
     glibc-headers \
     glog-devel \
+    hiredis-devel \
     libstdc++-devel \
     libsndfile-devel \
     make \
@@ -152,7 +162,7 @@ wget https://ossci-linux.s3.amazonaws.com/valgrind-${VALGRIND_VERSION}.tar.bz2
 tar -xjf valgrind-${VALGRIND_VERSION}.tar.bz2
 cd valgrind-${VALGRIND_VERSION}
 ./configure --prefix=/usr/local
-make -j$[$(nproc) - 2]
+make -j6
 sudo make install
 cd ../../
 rm -rf valgrind_build

.ci/docker/common/install_cache.sh

@@ -36,11 +36,14 @@ if [ -n "$ROCM_VERSION" ]; then
   curl --retry 3 http://repo.radeon.com/misc/.sccache_amd/sccache -o /opt/cache/bin/sccache
 else
   ID=$(grep -oP '(?<=^ID=).+' /etc/os-release | tr -d '"')
-  # TODO: Install the pre-built binary from S3 as building from source
-  # https://github.com/pytorch/sccache has started failing mysteriously
-  # in which sccache server couldn't start with the following error:
-  #   sccache: error: Invalid argument (os error 22)
-  install_binary
+  case "$ID" in
+    ubuntu)
+      install_ubuntu
+      ;;
+    *)
+      install_binary
+      ;;
+  esac
 fi
 chmod a+x /opt/cache/bin/sccache
 

.ci/docker/common/install_clang.sh

@@ -4,7 +4,10 @@ set -ex
 
 if [ -n "$CLANG_VERSION" ]; then
 
-  if [[ $CLANG_VERSION == 9 && $UBUNTU_VERSION == 18.04 ]]; then
+  if [[ $CLANG_VERSION == 7 && $UBUNTU_VERSION == 16.04 ]]; then
+    wget -O - https://apt.llvm.org/llvm-snapshot.gpg.key | sudo apt-key add -
+    sudo apt-add-repository "deb http://apt.llvm.org/xenial/ llvm-toolchain-xenial-7 main"
+  elif [[ $CLANG_VERSION == 9 && $UBUNTU_VERSION == 18.04 ]]; then
     sudo apt-get update
     # gpg-agent is not available by default on 18.04
     sudo apt-get install  -y --no-install-recommends gpg-agent
@@ -25,11 +28,11 @@ if [ -n "$CLANG_VERSION" ]; then
   fi
 
   # Use update-alternatives to make this version the default
+  # TODO: Decide if overriding gcc as well is a good idea
+  # update-alternatives --install /usr/bin/gcc gcc /usr/bin/clang-"$CLANG_VERSION" 50
+  # update-alternatives --install /usr/bin/g++ g++ /usr/bin/clang++-"$CLANG_VERSION" 50
   update-alternatives --install /usr/bin/clang clang /usr/bin/clang-"$CLANG_VERSION" 50
   update-alternatives --install /usr/bin/clang++ clang++ /usr/bin/clang++-"$CLANG_VERSION" 50
-  # Override cc/c++ to clang as well
-  update-alternatives --install /usr/bin/cc cc /usr/bin/clang 50
-  update-alternatives --install /usr/bin/c++ c++ /usr/bin/clang++ 50
 
   # clang's packaging is a little messed up (the runtime libs aren't
   # added into the linker path), so give it a little help

.ci/docker/common/install_conda.sh

@@ -7,21 +7,11 @@ if [ -n "$ANACONDA_PYTHON_VERSION" ]; then
   BASE_URL="https://repo.anaconda.com/miniconda"
 
   MAJOR_PYTHON_VERSION=$(echo "$ANACONDA_PYTHON_VERSION" | cut -d . -f 1)
-  MINOR_PYTHON_VERSION=$(echo "$ANACONDA_PYTHON_VERSION" | cut -d . -f 2)
 
-if [[ $(uname -m) == "aarch64" ]]; then
-  BASE_URL="https://github.com/conda-forge/miniforge/releases/latest/download"
   case "$MAJOR_PYTHON_VERSION" in
-    3)
-      CONDA_FILE="Miniforge3-Linux-aarch64.sh"
+    2)
+      CONDA_FILE="Miniconda2-latest-Linux-x86_64.sh"
     ;;
-    *)
-      echo "Unsupported ANACONDA_PYTHON_VERSION: $ANACONDA_PYTHON_VERSION"
-      exit 1
-      ;;
-  esac
-else
-  case "$MAJOR_PYTHON_VERSION" in
     3)
       CONDA_FILE="Miniconda3-latest-Linux-x86_64.sh"
     ;;
@@ -30,7 +20,6 @@ else
       exit 1
       ;;
   esac
-fi
 
   mkdir -p /opt/conda
   chown jenkins:jenkins /opt/conda
@@ -57,45 +46,29 @@ fi
   # Uncomment the below when resolved to track the latest conda update
   # as_jenkins conda update -y -n base conda
 
-  if [[ $(uname -m) == "aarch64" ]]; then
-    export SYSROOT_DEP="sysroot_linux-aarch64=2.17"
-  else
-    export SYSROOT_DEP="sysroot_linux-64=2.17"
-  fi
-
   # Install correct Python version
-  # Also ensure sysroot is using a modern GLIBC to match system compilers
-  as_jenkins conda create -n py_$ANACONDA_PYTHON_VERSION -y\
-             python="$ANACONDA_PYTHON_VERSION" \
-             ${SYSROOT_DEP}
-
-  # libstdcxx from conda default channels are too old, we need GLIBCXX_3.4.30
-  # which is provided in libstdcxx 12 and up.
-  conda_install libstdcxx-ng=12.3.0 -c conda-forge
+  as_jenkins conda create -n py_$ANACONDA_PYTHON_VERSION -y python="$ANACONDA_PYTHON_VERSION"
 
   # Install PyTorch conda deps, as per https://github.com/pytorch/pytorch README
-  if [[ $(uname -m) == "aarch64" ]]; then
-    CONDA_COMMON_DEPS="astunparse pyyaml setuptools openblas==0.3.25=*openmp* ninja==1.11.1 scons==4.5.2"
-
-    if [ "$ANACONDA_PYTHON_VERSION" = "3.8" ]; then
-      conda_install numpy=1.24.4 ${CONDA_COMMON_DEPS}
-    else
-      conda_install numpy=1.26.2 ${CONDA_COMMON_DEPS}
-    fi
+  CONDA_COMMON_DEPS="astunparse pyyaml mkl=2021.4.0 mkl-include=2021.4.0 setuptools"
+  if [ "$ANACONDA_PYTHON_VERSION" = "3.11" ]; then
+    # Install llvm-8 as it is required to compile llvmlite-0.30.0 from source
+    # TODO: Stop using `-c malfet`
+    conda_install numpy=1.23.5 ${CONDA_COMMON_DEPS} llvmdev=8.0.0 -c malfet
+  elif [ "$ANACONDA_PYTHON_VERSION" = "3.10" ]; then
+    # Install llvm-8 as it is required to compile llvmlite-0.30.0 from source
+    conda_install numpy=1.21.2 ${CONDA_COMMON_DEPS} llvmdev=8.0.0
+  elif [ "$ANACONDA_PYTHON_VERSION" = "3.9" ]; then
+    # Install llvm-8 as it is required to compile llvmlite-0.30.0 from source
+    conda_install numpy=1.19.2 ${CONDA_COMMON_DEPS} llvmdev=8.0.0
+  elif [ "$ANACONDA_PYTHON_VERSION" = "3.8" ]; then
+    # Install llvm-8 as it is required to compile llvmlite-0.30.0 from source
+    conda_install numpy=1.18.5 ${CONDA_COMMON_DEPS} llvmdev=8.0.0
   else
-    CONDA_COMMON_DEPS="astunparse pyyaml mkl=2021.4.0 mkl-include=2021.4.0 setuptools"
-
-    if [ "$ANACONDA_PYTHON_VERSION" = "3.11" ] || [ "$ANACONDA_PYTHON_VERSION" = "3.12" ]; then
-      conda_install numpy=1.26.0 ${CONDA_COMMON_DEPS}
-    else
-      conda_install numpy=1.21.2 ${CONDA_COMMON_DEPS}
-    fi
+    # Install `typing-extensions` for 3.7
+    conda_install numpy=1.18.5 ${CONDA_COMMON_DEPS} typing-extensions
   fi
 
-  # Install llvm-8 as it is required to compile llvmlite-0.30.0 from source
-  # and libpython-static for torch deploy
-  conda_install llvmdev=8.0.0 "libpython-static=${ANACONDA_PYTHON_VERSION}"
-
   # Use conda cmake in some cases. Conda cmake will be newer than our supported
   # min version (3.5 for xenial and 3.10 for bionic), so we only do it in those
   # following builds that we know should use conda. Specifically, Ubuntu bionic
@@ -113,14 +86,12 @@ fi
   # Install some other packages, including those needed for Python test reporting
   pip_install -r /opt/conda/requirements-ci.txt
 
-  pip_install -U scikit-learn
-
-  if [ -n "$DOCS" ]; then
-    apt-get update
-    apt-get -y install expect-dev
-
-    # We are currently building docs with python 3.8 (min support version)
-    pip_install -r /opt/conda/requirements-docs.txt
+  # Update scikit-learn to a python-3.8 compatible version
+  if [[ $(python -c "import sys; print(int(sys.version_info >= (3, 8)))") == "1" ]]; then
+    pip_install -U scikit-learn
+  else
+    # Pinned scikit-learn due to https://github.com/scikit-learn/scikit-learn/issues/14485 (affects gcc 5.5 only)
+    pip_install scikit-learn==0.20.3
   fi
 
   popd

.ci/docker/common/install_cudnn.sh

@@ -1,22 +1,27 @@
 #!/bin/bash
 
-if [[ -n "${CUDNN_VERSION}" ]]; then
+if [[ ${CUDNN_VERSION} == 8 ]]; then
     # cuDNN license: https://developer.nvidia.com/cudnn/license_agreement
-    mkdir tmp_cudnn
-    pushd tmp_cudnn
-    if [[ ${CUDA_VERSION:0:2} == "12" ]]; then
-        CUDNN_NAME="cudnn-linux-x86_64-9.1.0.70_cuda12-archive"
-    elif [[ ${CUDA_VERSION:0:2} == "11" ]]; then
-        CUDNN_NAME="cudnn-linux-x86_64-9.1.0.70_cuda11-archive"
+    mkdir tmp_cudnn && cd tmp_cudnn
+    CUDNN_NAME="cudnn-linux-x86_64-8.3.2.44_cuda11.5-archive"
+    if [[ ${CUDA_VERSION:0:4} == "11.7" ]]; then
+        CUDNN_NAME="cudnn-linux-x86_64-8.5.0.96_cuda11-archive"
+        curl --retry 3 -OLs https://ossci-linux.s3.amazonaws.com/${CUDNN_NAME}.tar.xz
+    elif [[ ${CUDA_VERSION:0:4} == "11.8" ]]; then
+        CUDNN_NAME="cudnn-linux-x86_64-8.7.0.84_cuda11-archive"
+        curl --retry 3 -OLs https://developer.download.nvidia.com/compute/redist/cudnn/v8.7.0/local_installers/11.8/${CUDNN_NAME}.tar.xz
     else
-        print "Unsupported CUDA version ${CUDA_VERSION}"
-        exit 1
+        curl --retry 3 -OLs https://developer.download.nvidia.com/compute/redist/cudnn/v8.3.2/local_installers/11.5/${CUDNN_NAME}.tar.xz
     fi
-    curl --retry 3 -OLs https://developer.download.nvidia.com/compute/cudnn/redist/cudnn/linux-x86_64/${CUDNN_NAME}.tar.xz
+
     tar xf ${CUDNN_NAME}.tar.xz
+    cp -a ${CUDNN_NAME}/include/* /usr/include/
     cp -a ${CUDNN_NAME}/include/* /usr/local/cuda/include/
+    cp -a ${CUDNN_NAME}/include/* /usr/include/x86_64-linux-gnu/
+
     cp -a ${CUDNN_NAME}/lib/* /usr/local/cuda/lib64/
-    popd
+    cp -a ${CUDNN_NAME}/lib/* /usr/lib/x86_64-linux-gnu/
+    cd ..
     rm -rf tmp_cudnn
     ldconfig
 fi

.ci/docker/common/install_cusparselt.sh

@@ -1,26 +0,0 @@
-#!/bin/bash
-
-set -ex
-
-# cuSPARSELt license: https://docs.nvidia.com/cuda/cusparselt/license.html
-mkdir tmp_cusparselt && cd tmp_cusparselt
-
-if [[ ${CUDA_VERSION:0:4} =~ ^12\.[1-4]$ ]]; then
-    arch_path='sbsa'
-    export TARGETARCH=${TARGETARCH:-$(uname -m)}
-    if [ ${TARGETARCH} = 'amd64' ] || [ "${TARGETARCH}" = 'x86_64' ]; then
-        arch_path='x86_64'
-    fi
-    CUSPARSELT_NAME="libcusparse_lt-linux-${arch_path}-0.5.2.1-archive"
-    curl --retry 3 -OLs https://developer.download.nvidia.com/compute/cusparselt/redist/libcusparse_lt/linux-${arch_path}/${CUSPARSELT_NAME}.tar.xz
-elif [[ ${CUDA_VERSION:0:4} == "11.8" ]]; then
-    CUSPARSELT_NAME="libcusparse_lt-linux-x86_64-0.4.0.7-archive"
-    curl --retry 3 -OLs https://developer.download.nvidia.com/compute/cusparselt/redist/libcusparse_lt/linux-x86_64/${CUSPARSELT_NAME}.tar.xz
-fi
-
-tar xf ${CUSPARSELT_NAME}.tar.xz
-cp -a ${CUSPARSELT_NAME}/include/* /usr/local/cuda/include/
-cp -a ${CUSPARSELT_NAME}/lib/* /usr/local/cuda/lib64/
-cd ..
-rm -rf tmp_cusparselt
-ldconfig

.ci/docker/common/install_db.sh

@@ -4,6 +4,11 @@ set -ex
 
 install_ubuntu() {
   apt-get update
+  apt-get install -y --no-install-recommends \
+          libhiredis-dev \
+          libleveldb-dev \
+          liblmdb-dev \
+          libsnappy-dev
 
   # Cleanup
   apt-get autoclean && apt-get clean
@@ -15,6 +20,12 @@ install_centos() {
   # See http://fedoraproject.org/wiki/EPEL
   yum --enablerepo=extras install -y epel-release
 
+  yum install -y \
+      hiredis-devel \
+      leveldb-devel \
+      lmdb-devel \
+      snappy-devel
+
   # Cleanup
   yum clean all
   rm -rf /var/cache/yum

.ci/docker/common/install_docs_reqs.sh

@@ -7,7 +7,7 @@ if [ -n "$KATEX" ]; then
   # Ignore error if gpg-agent doesn't exist (for Ubuntu 16.04)
   apt-get install -y gpg-agent || :
 
-  curl --retry 3 -sL https://deb.nodesource.com/setup_16.x | sudo -E bash -
+  curl --retry 3 -sL https://deb.nodesource.com/setup_12.x | sudo -E bash -
   sudo apt-get install -y nodejs
 
   curl --retry 3 -sS https://dl.yarnpkg.com/debian/pubkey.gpg | sudo apt-key add -

.ci/docker/common/install_executorch.sh

@@ -1,61 +0,0 @@
-#!/bin/bash
-
-set -ex
-
-source "$(dirname "${BASH_SOURCE[0]}")/common_utils.sh"
-
-clone_executorch() {
-  EXECUTORCH_PINNED_COMMIT=$(get_pinned_commit executorch)
-
-  # Clone the Executorch
-  git clone https://github.com/pytorch/executorch.git
-
-  # and fetch the target commit
-  pushd executorch
-  git checkout "${EXECUTORCH_PINNED_COMMIT}"
-  git submodule update --init
-  popd
-
-  chown -R jenkins executorch
-}
-
-install_buck2() {
-  pushd executorch/.ci/docker
-
-  BUCK2_VERSION=$(cat ci_commit_pins/buck2.txt)
-  source common/install_buck.sh
-
-  popd
-}
-
-install_conda_dependencies() {
-  pushd executorch/.ci/docker
-  # Install conda dependencies like flatbuffer
-  conda_install --file conda-env-ci.txt
-  popd
-}
-
-install_pip_dependencies() {
-  pushd executorch/.ci/docker
-  # Install all Python dependencies
-  pip_install -r requirements-ci.txt
-  popd
-}
-
-setup_executorch() {
-  pushd executorch
-  source .ci/scripts/utils.sh
-
-  install_flatc_from_source
-  pip_install .
-
-  # Make sure that all the newly generate files are owned by Jenkins
-  chown -R jenkins .
-  popd
-}
-
-clone_executorch
-install_buck2
-install_conda_dependencies
-install_pip_dependencies
-setup_executorch

.ci/docker/common/install_gcc.sh

@@ -7,10 +7,17 @@ if [ -n "$GCC_VERSION" ]; then
   # Need the official toolchain repo to get alternate packages
   add-apt-repository ppa:ubuntu-toolchain-r/test
   apt-get update
-  apt-get install -y g++-$GCC_VERSION
-  update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-"$GCC_VERSION" 50
-  update-alternatives --install /usr/bin/g++ g++ /usr/bin/g++-"$GCC_VERSION" 50
-  update-alternatives --install /usr/bin/gcov gcov /usr/bin/gcov-"$GCC_VERSION" 50
+  if [[ "$UBUNTU_VERSION" == "16.04" && "${GCC_VERSION:0:1}" == "5" ]]; then
+    apt-get install -y g++-5=5.4.0-6ubuntu1~16.04.12
+    update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-5 50
+    update-alternatives --install /usr/bin/g++ g++ /usr/bin/g++-5 50
+    update-alternatives --install /usr/bin/gcov gcov /usr/bin/gcov-5 50
+  else
+    apt-get install -y g++-$GCC_VERSION
+    update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-"$GCC_VERSION" 50
+    update-alternatives --install /usr/bin/g++ g++ /usr/bin/g++-"$GCC_VERSION" 50
+    update-alternatives --install /usr/bin/gcov gcov /usr/bin/gcov-"$GCC_VERSION" 50
+  fi
 
 
   # Cleanup package manager

.ci/docker/common/install_inductor_benchmark_deps.sh

@@ -1,26 +0,0 @@
-#!/bin/bash
-
-set -ex
-
-source "$(dirname "${BASH_SOURCE[0]}")/common_utils.sh"
-
-function install_huggingface() {
-  local version
-  commit=$(get_pinned_commit huggingface)
-  pip_install pandas==2.0.3
-  pip_install "git+https://github.com/huggingface/transformers@${commit}"
-}
-
-function install_timm() {
-  local commit
-  commit=$(get_pinned_commit timm)
-  pip_install pandas==2.0.3
-  pip_install "git+https://github.com/huggingface/pytorch-image-models@${commit}"
-  # Clean up
-  conda_run pip uninstall -y cmake torch torchvision triton
-}
-
-# Pango is needed for weasyprint which is needed for doctr
-conda_install pango
-install_huggingface
-install_timm

.ci/docker/common/install_onnx.sh

@@ -1,51 +0,0 @@
-#!/bin/bash
-
-set -ex
-
-source "$(dirname "${BASH_SOURCE[0]}")/common_utils.sh"
-
-retry () {
-    "$@" || (sleep 10 && "$@") || (sleep 20 && "$@") || (sleep 40 && "$@")
-}
-
-# A bunch of custom pip dependencies for ONNX
-pip_install \
-  beartype==0.15.0 \
-  filelock==3.9.0 \
-  flatbuffers==2.0 \
-  mock==5.0.1 \
-  ninja==1.10.2 \
-  networkx==2.0 \
-  numpy==1.24.2
-
-# ONNXRuntime should be installed before installing
-# onnx-weekly. Otherwise, onnx-weekly could be
-# overwritten by onnx.
-pip_install \
-  parameterized==0.8.1 \
-  pytest-cov==4.0.0 \
-  pytest-subtests==0.10.0 \
-  tabulate==0.9.0 \
-  transformers==4.36.2
-
-pip_install coloredlogs packaging
-
-pip_install onnxruntime==1.18
-pip_install onnx==1.16.0
-# pip_install "onnxscript@git+https://github.com/microsoft/onnxscript@3e869ef8ccf19b5ebd21c10d3e9c267c9a9fa729" --no-deps
-pip_install onnxscript==0.1.0.dev20240523 --no-deps
-
-# Cache the transformers model to be used later by ONNX tests. We need to run the transformers
-# package to download the model. By default, the model is cached at ~/.cache/huggingface/hub/
-IMPORT_SCRIPT_FILENAME="/tmp/onnx_import_script.py"
-as_jenkins echo 'import transformers; transformers.AutoModel.from_pretrained("sshleifer/tiny-gpt2"); transformers.AutoTokenizer.from_pretrained("sshleifer/tiny-gpt2"); transformers.AutoModelForSpeechSeq2Seq.from_pretrained("openai/whisper-large-v3");' > "${IMPORT_SCRIPT_FILENAME}"
-
-# Need a PyTorch version for transformers to work
-pip_install --pre torch --index-url https://download.pytorch.org/whl/nightly/cpu
-# Very weird quoting behavior here https://github.com/conda/conda/issues/10972,
-# so echo the command to a file and run the file instead
-conda_run python "${IMPORT_SCRIPT_FILENAME}"
-
-# Cleaning up
-conda_run pip uninstall -y torch
-rm "${IMPORT_SCRIPT_FILENAME}" || true

.ci/docker/common/install_openssl.sh

@@ -9,8 +9,7 @@ tar xf "${OPENSSL}.tar.gz"
 cd "${OPENSSL}"
 ./config --prefix=/opt/openssl -d '-Wl,--enable-new-dtags,-rpath,$(LIBRPATH)'
 # NOTE: openssl install errors out when built with the -j option
-NPROC=$[$(nproc) - 2]
-make -j${NPROC}; make install_sw
+make -j6; make install_sw
 # Link the ssl libraries to the /usr/lib folder.
 sudo ln -s /opt/openssl/lib/lib* /usr/lib
 cd ..

.ci/docker/common/install_protobuf.sh

@@ -2,18 +2,55 @@
 
 set -ex
 
-pb_dir="/usr/temp_pb_install_dir"
-mkdir -p $pb_dir
+# This function installs protobuf 3.17
+install_protobuf_317() {
+  pb_dir="/usr/temp_pb_install_dir"
+  mkdir -p $pb_dir
 
-# On the nvidia/cuda:9-cudnn7-devel-centos7 image we need this symlink or
-# else it will fail with
-#   g++: error: ./../lib64/crti.o: No such file or directory
-ln -s /usr/lib64 "$pb_dir/lib64"
+  # On the nvidia/cuda:9-cudnn7-devel-centos7 image we need this symlink or
+  # else it will fail with
+  #   g++: error: ./../lib64/crti.o: No such file or directory
+  ln -s /usr/lib64 "$pb_dir/lib64"
 
-curl -LO "https://github.com/protocolbuffers/protobuf/releases/download/v3.17.3/protobuf-all-3.17.3.tar.gz" --retry 3
+  curl -LO "https://github.com/protocolbuffers/protobuf/releases/download/v3.17.3/protobuf-all-3.17.3.tar.gz" --retry 3
+  tar -xvz -C "$pb_dir" --strip-components 1 -f protobuf-all-3.17.3.tar.gz
+  # -j6 to balance memory usage and speed.
+  # naked `-j` seems to use too much memory.
+  pushd "$pb_dir" && ./configure && make -j6 && make -j6 check && sudo make -j6 install && sudo ldconfig
+  popd
+  rm -rf $pb_dir
+}
 
-tar -xvz --no-same-owner -C "$pb_dir" --strip-components 1 -f protobuf-all-3.17.3.tar.gz
-NPROC=$[$(nproc) - 2]
-pushd "$pb_dir" && ./configure && make -j${NPROC} && make -j${NPROC} check && sudo make -j${NRPOC} install && sudo ldconfig
-popd
-rm -rf $pb_dir
+install_ubuntu() {
+  # Ubuntu 14.04 has cmake 2.8.12 as the default option, so we will
+  # install cmake3 here and use cmake3.
+  apt-get update
+  if [[ "$UBUNTU_VERSION" == 14.04 ]]; then
+    apt-get install -y --no-install-recommends cmake3
+  fi
+
+  # Cleanup
+  apt-get autoclean && apt-get clean
+  rm -rf /var/lib/apt/lists/* /tmp/* /var/tmp/*
+
+  install_protobuf_317
+}
+
+install_centos() {
+  install_protobuf_317
+}
+
+# Install base packages depending on the base OS
+ID=$(grep -oP '(?<=^ID=).+' /etc/os-release | tr -d '"')
+case "$ID" in
+  ubuntu)
+    install_ubuntu
+    ;;
+  centos)
+    install_centos
+    ;;
+  *)
+    echo "Unable to determine OS..."
+    exit 1
+    ;;
+esac

.ci/docker/common/install_rocm.sh

@@ -6,6 +6,9 @@ ver() {
     printf "%3d%03d%03d%03d" $(echo "$1" | tr '.' ' ');
 }
 
+# Map ROCm version to AMDGPU version
+declare -A AMDGPU_VERSIONS=( ["5.0"]="21.50" ["5.1.1"]="22.10.1" ["5.2"]="22.20" )
+
 install_ubuntu() {
     apt-get update
     if [[ $UBUNTU_VERSION == 18.04 ]]; then
@@ -23,14 +26,31 @@ install_ubuntu() {
     apt-get install -y libc++1
     apt-get install -y libc++abi1
 
-    # Add amdgpu repository
-    UBUNTU_VERSION_NAME=`cat /etc/os-release | grep UBUNTU_CODENAME | awk -F= '{print $2}'`
-    echo "deb [arch=amd64] https://repo.radeon.com/amdgpu/${ROCM_VERSION}/ubuntu ${UBUNTU_VERSION_NAME} main" > /etc/apt/sources.list.d/amdgpu.list
+    if [[ $(ver $ROCM_VERSION) -ge $(ver 4.5) ]]; then
+        # Add amdgpu repository
+        UBUNTU_VERSION_NAME=`cat /etc/os-release | grep UBUNTU_CODENAME | awk -F= '{print $2}'`
+        local amdgpu_baseurl
+        if [[ $(ver $ROCM_VERSION) -ge $(ver 5.3) ]]; then
+          amdgpu_baseurl="https://repo.radeon.com/amdgpu/${ROCM_VERSION}/ubuntu"
+        else
+          amdgpu_baseurl="https://repo.radeon.com/amdgpu/${AMDGPU_VERSIONS[$ROCM_VERSION]}/ubuntu"
+        fi
+        echo "deb [arch=amd64] ${amdgpu_baseurl} ${UBUNTU_VERSION_NAME} main" > /etc/apt/sources.list.d/amdgpu.list
+    fi
+
+    ROCM_REPO="ubuntu"
+    if [[ $(ver $ROCM_VERSION) -lt $(ver 4.2) ]]; then
+        ROCM_REPO="xenial"
+    fi
+
+    if [[ $(ver $ROCM_VERSION) -ge $(ver 5.3) ]]; then
+        ROCM_REPO="${UBUNTU_VERSION_NAME}"
+    fi
 
     # Add rocm repository
     wget -qO - http://repo.radeon.com/rocm/rocm.gpg.key | apt-key add -
     local rocm_baseurl="http://repo.radeon.com/rocm/apt/${ROCM_VERSION}"
-    echo "deb [arch=amd64] ${rocm_baseurl} ${UBUNTU_VERSION_NAME} main" > /etc/apt/sources.list.d/rocm.list
+    echo "deb [arch=amd64] ${rocm_baseurl} ${ROCM_REPO} main" > /etc/apt/sources.list.d/rocm.list
     apt-get update --allow-insecure-repositories
 
     DEBIAN_FRONTEND=noninteractive apt-get install -y --allow-unauthenticated \
@@ -39,29 +59,17 @@ install_ubuntu() {
                    rocm-libs \
                    rccl \
                    rocprofiler-dev \
-                   roctracer-dev \
-                   amd-smi-lib
+                   roctracer-dev
 
-    if [[ $(ver $ROCM_VERSION) -ge $(ver 6.1) ]]; then
-        DEBIAN_FRONTEND=noninteractive apt-get install -y --allow-unauthenticated rocm-llvm-dev
-    fi
-
-    # precompiled miopen kernels added in ROCm 3.5, renamed in ROCm 5.5
-    # search for all unversioned packages
+    # precompiled miopen kernels added in ROCm 3.5; search for all unversioned packages
     # if search fails it will abort this script; use true to avoid case where search fails
-    MIOPENHIPGFX=$(apt-cache search --names-only miopen-hip-gfx | awk '{print $1}' | grep -F -v . || true)
-    if [[ "x${MIOPENHIPGFX}" = x ]]; then
-      echo "miopen-hip-gfx package not available" && exit 1
+    MIOPENKERNELS=$(apt-cache search --names-only miopenkernels | awk '{print $1}' | grep -F -v . || true)
+    if [[ "x${MIOPENKERNELS}" = x ]]; then
+      echo "miopenkernels package not available"
     else
-      DEBIAN_FRONTEND=noninteractive apt-get install -y --allow-unauthenticated ${MIOPENHIPGFX}
+      DEBIAN_FRONTEND=noninteractive apt-get install -y --allow-unauthenticated ${MIOPENKERNELS}
     fi
 
-    # ROCm 6.0 had a regression where journal_mode was enabled on the kdb files resulting in permission errors at runtime
-    for kdb in /opt/rocm/share/miopen/db/*.kdb
-    do
-        sqlite3 $kdb "PRAGMA journal_mode=off; PRAGMA VACUUM;"
-    done
-
     # Cleanup
     apt-get autoclean && apt-get clean
     rm -rf /var/lib/apt/lists/* /tmp/* /var/tmp/*
@@ -77,19 +85,25 @@ install_centos() {
   yum install -y epel-release
   yum install -y dkms kernel-headers-`uname -r` kernel-devel-`uname -r`
 
-  # Add amdgpu repository
-  local amdgpu_baseurl
-  if [[ $OS_VERSION == 9 ]]; then
-      amdgpu_baseurl="https://repo.radeon.com/amdgpu/${ROCM_VERSION}/rhel/9.0/main/x86_64"
-  else
-      amdgpu_baseurl="https://repo.radeon.com/amdgpu/${ROCM_VERSION}/rhel/7.9/main/x86_64"
+  if [[ $(ver $ROCM_VERSION) -ge $(ver 4.5) ]]; then
+      # Add amdgpu repository
+      local amdgpu_baseurl
+      if [[ $OS_VERSION == 9 ]]; then
+          amdgpu_baseurl="https://repo.radeon.com/amdgpu/${AMDGPU_VERSIONS[$ROCM_VERSION]}/rhel/9.0/main/x86_64"
+      else
+        if [[ $(ver $ROCM_VERSION) -ge $(ver 5.3) ]]; then
+          amdgpu_baseurl="https://repo.radeon.com/amdgpu/${ROCM_VERSION}/rhel/7.9/main/x86_64"
+        else
+          amdgpu_baseurl="https://repo.radeon.com/amdgpu/${AMDGPU_VERSIONS[$ROCM_VERSION]}/rhel/7.9/main/x86_64"
+        fi
+      fi
+      echo "[AMDGPU]" > /etc/yum.repos.d/amdgpu.repo
+      echo "name=AMDGPU" >> /etc/yum.repos.d/amdgpu.repo
+      echo "baseurl=${amdgpu_baseurl}" >> /etc/yum.repos.d/amdgpu.repo
+      echo "enabled=1" >> /etc/yum.repos.d/amdgpu.repo
+      echo "gpgcheck=1" >> /etc/yum.repos.d/amdgpu.repo
+      echo "gpgkey=http://repo.radeon.com/rocm/rocm.gpg.key" >> /etc/yum.repos.d/amdgpu.repo
   fi
-  echo "[AMDGPU]" > /etc/yum.repos.d/amdgpu.repo
-  echo "name=AMDGPU" >> /etc/yum.repos.d/amdgpu.repo
-  echo "baseurl=${amdgpu_baseurl}" >> /etc/yum.repos.d/amdgpu.repo
-  echo "enabled=1" >> /etc/yum.repos.d/amdgpu.repo
-  echo "gpgcheck=1" >> /etc/yum.repos.d/amdgpu.repo
-  echo "gpgkey=http://repo.radeon.com/rocm/rocm.gpg.key" >> /etc/yum.repos.d/amdgpu.repo
 
   local rocm_baseurl="http://repo.radeon.com/rocm/yum/${ROCM_VERSION}"
   echo "[ROCm]" > /etc/yum.repos.d/rocm.repo
@@ -107,23 +121,7 @@ install_centos() {
                    rocm-libs \
                    rccl \
                    rocprofiler-dev \
-                   roctracer-dev \
-                   amd-smi-lib
-
-  # precompiled miopen kernels; search for all unversioned packages
-  # if search fails it will abort this script; use true to avoid case where search fails
-  MIOPENHIPGFX=$(yum -q search miopen-hip-gfx | grep miopen-hip-gfx | awk '{print $1}'| grep -F kdb. || true)
-  if [[ "x${MIOPENHIPGFX}" = x ]]; then
-    echo "miopen-hip-gfx package not available" && exit 1
-  else
-    yum install -y ${MIOPENHIPGFX}
-  fi
-
-  # ROCm 6.0 had a regression where journal_mode was enabled on the kdb files resulting in permission errors at runtime
-  for kdb in /opt/rocm/share/miopen/db/*.kdb
-  do
-      sqlite3 $kdb "PRAGMA journal_mode=off; PRAGMA VACUUM;"
-  done
+                   roctracer-dev
 
   # Cleanup
   yum clean all

.ci/docker/common/install_rocm_magma.sh

@@ -5,10 +5,8 @@ set -ex
 # "install" hipMAGMA into /opt/rocm/magma by copying after build
 git clone https://bitbucket.org/icl/magma.git
 pushd magma
-
-# Version 2.7.2 + ROCm related updates
-git checkout a1625ff4d9bc362906bd01f805dbbe12612953f6
-
+# Fixes memory leaks of magma found while executing linalg UTs
+git checkout 5959b8783e45f1809812ed96ae762f38ee701972
 cp make.inc-examples/make.inc.hip-gcc-mkl make.inc
 echo 'LIBDIR += -L$(MKLROOT)/lib' >> make.inc
 echo 'LIB += -Wl,--enable-new-dtags -Wl,--rpath,/opt/rocm/lib -Wl,--rpath,$(MKLROOT)/lib -Wl,--rpath,/opt/rocm/magma/lib' >> make.inc
@@ -20,7 +18,7 @@ else
   amdgpu_targets=`rocm_agent_enumerator | grep -v gfx000 | sort -u | xargs`
 fi
 for arch in $amdgpu_targets; do
-  echo "DEVCCFLAGS += --offload-arch=$arch" >> make.inc
+  echo "DEVCCFLAGS += --amdgpu-target=$arch" >> make.inc
 done
 # hipcc with openmp flag may cause isnan() on __device__ not to be found; depending on context, compiler may attempt to match with host definition
 sed -i 's/^FOPENMP/#FOPENMP/g' make.inc

.ci/docker/common/install_thrift.sh

@@ -0,0 +1,14 @@
+apt-get update
+apt-get install -y sudo wget libboost-dev libboost-test-dev libboost-program-options-dev libboost-filesystem-dev libboost-thread-dev libevent-dev automake libtool flex bison pkg-config g++ libssl-dev
+wget https://www-us.apache.org/dist/thrift/0.12.0/thrift-0.12.0.tar.gz
+tar -xvf thrift-0.12.0.tar.gz
+cd thrift-0.12.0
+for file in ./compiler/cpp/Makefile*; do
+  sed -i 's/\-Werror//' $file
+done
+./bootstrap.sh
+./configure --without-php --without-java --without-python --without-nodejs --without-go --without-ruby
+sudo make
+sudo make install
+cd ..
+rm thrift-0.12.0.tar.gz

.ci/docker/common/install_triton.sh

@@ -1,72 +0,0 @@
-#!/bin/bash
-
-set -ex
-
-source "$(dirname "${BASH_SOURCE[0]}")/common_utils.sh"
-
-get_conda_version() {
-  as_jenkins conda list -n py_$ANACONDA_PYTHON_VERSION | grep -w $* | head -n 1 | awk '{print $2}'
-}
-
-conda_reinstall() {
-  as_jenkins conda install -q -n py_$ANACONDA_PYTHON_VERSION -y --force-reinstall $*
-}
-
-if [ -n "${ROCM_VERSION}" ]; then
-  TRITON_REPO="https://github.com/openai/triton"
-  TRITON_TEXT_FILE="triton-rocm"
-elif [ -n "${XPU_VERSION}" ]; then
-  TRITON_REPO="https://github.com/intel/intel-xpu-backend-for-triton"
-  TRITON_TEXT_FILE="triton-xpu"
-else
-  TRITON_REPO="https://github.com/openai/triton"
-  TRITON_TEXT_FILE="triton"
-fi
-
-# The logic here is copied from .ci/pytorch/common_utils.sh
-TRITON_PINNED_COMMIT=$(get_pinned_commit ${TRITON_TEXT_FILE})
-
-if [ -n "${UBUNTU_VERSION}" ];then
-    apt update
-    apt-get install -y gpg-agent
-fi
-
-if [ -n "${CONDA_CMAKE}" ]; then
-  # Keep the current cmake and numpy version here, so we can reinstall them later
-  CMAKE_VERSION=$(get_conda_version cmake)
-  NUMPY_VERSION=$(get_conda_version numpy)
-fi
-
-if [ -z "${MAX_JOBS}" ]; then
-    export MAX_JOBS=$(nproc)
-fi
-
-if [ -n "${UBUNTU_VERSION}" ] && [ -n "${GCC_VERSION}" ] && [[ "${GCC_VERSION}" == "7" ]]; then
-  # Triton needs at least gcc-9 to build
-  apt-get install -y g++-9
-
-  CXX=g++-9 pip_install "git+${TRITON_REPO}@${TRITON_PINNED_COMMIT}#subdirectory=python"
-elif [ -n "${UBUNTU_VERSION}" ] && [ -n "${CLANG_VERSION}" ]; then
-  # Triton needs <filesystem> which surprisingly is not available with clang-9 toolchain
-  add-apt-repository -y ppa:ubuntu-toolchain-r/test
-  apt-get install -y g++-9
-
-  CXX=g++-9 pip_install "git+${TRITON_REPO}@${TRITON_PINNED_COMMIT}#subdirectory=python"
-else
-  pip_install "git+${TRITON_REPO}@${TRITON_PINNED_COMMIT}#subdirectory=python"
-fi
-
-if [ -n "${CONDA_CMAKE}" ]; then
-  # TODO: This is to make sure that the same cmake and numpy version from install conda
-  # script is used. Without this step, the newer cmake version (3.25.2) downloaded by
-  # triton build step via pip will fail to detect conda MKL. Once that issue is fixed,
-  # this can be removed.
-  #
-  # The correct numpy version also needs to be set here because conda claims that it
-  # causes inconsistent environment.  Without this, conda will attempt to install the
-  # latest numpy version, which fails ASAN tests with the following import error: Numba
-  # needs NumPy 1.20 or less.
-  conda_reinstall cmake="${CMAKE_VERSION}"
-  # Note that we install numpy with pip as conda might not have the version we want
-  pip_install --force-reinstall numpy=="${NUMPY_VERSION}"
-fi

.ci/docker/common/install_ucc.sh

@@ -36,12 +36,7 @@ function install_ucc() {
   git submodule update --init --recursive
 
   ./autogen.sh
-  # We only run distributed tests on Tesla M60 and A10G
-  NVCC_GENCODE="-gencode=arch=compute_52,code=sm_52 -gencode=arch=compute_86,code=compute_86"
-  ./configure --prefix=$UCC_HOME          \
-    --with-ucx=$UCX_HOME                  \
-    --with-cuda=$with_cuda                \
-    --with-nvcc-gencode="${NVCC_GENCODE}"
+  ./configure --prefix=$UCC_HOME --with-ucx=$UCX_HOME --with-cuda=$with_cuda
   time make -j
   sudo make install
 

.ci/docker/common/install_vision.sh

@@ -5,7 +5,8 @@ set -ex
 install_ubuntu() {
   apt-get update
   apt-get install -y --no-install-recommends \
-          libopencv-dev
+          libopencv-dev \
+          libavcodec-dev
 
   # Cleanup
   apt-get autoclean && apt-get clean
@@ -18,7 +19,8 @@ install_centos() {
   yum --enablerepo=extras install -y epel-release
 
   yum install -y \
-      opencv-devel
+      opencv-devel \
+      ffmpeg-devel
 
   # Cleanup
   yum clean all
@@ -41,6 +43,3 @@ case "$ID" in
     exit 1
     ;;
 esac
-
-# Cache vision models used by the test
-source "$(dirname "${BASH_SOURCE[0]}")/cache_vision_models.sh"

.ci/docker/common/install_xpu.sh

@@ -1,114 +0,0 @@
-#!/bin/bash
-set -xe
-
-
-# Intel® software for general purpose GPU capabilities.
-# Refer to https://www.intel.com/content/www/us/en/developer/articles/tool/pytorch-prerequisites-for-intel-gpus.html
-
-# Users should update to the latest version as it becomes available
-
-function install_ubuntu() {
-    apt-get update -y
-    apt-get install -y gpg-agent wget
-
-    # Set up the repository. To do this, download the key to the system keyring
-    wget -qO - https://repositories.intel.com/gpu/intel-graphics.key \
-        | gpg --dearmor --output /usr/share/keyrings/intel-graphics.gpg
-    wget -qO - https://apt.repos.intel.com/intel-gpg-keys/GPG-PUB-KEY-INTEL-SW-PRODUCTS.PUB \
-        | gpg --dearmor --output /usr/share/keyrings/intel-for-pytorch-gpu-dev-keyring.gpg
-
-    # Add the signed entry to APT sources and configure the APT client to use the Intel repository
-    echo "deb [arch=amd64 signed-by=/usr/share/keyrings/intel-graphics.gpg] \
-        https://repositories.intel.com/gpu/ubuntu jammy/lts/2350 unified" \
-        | tee /etc/apt/sources.list.d/intel-gpu-jammy.list
-    echo "deb [signed-by=/usr/share/keyrings/intel-for-pytorch-gpu-dev-keyring.gpg] \
-        https://apt.repos.intel.com/intel-for-pytorch-gpu-dev all main" \
-        | tee /etc/apt/sources.list.d/intel-for-pytorch-gpu-dev.list
-
-    # Update the packages list and repository index
-    apt-get update
-
-    # The xpu-smi packages
-    apt-get install -y flex bison xpu-smi
-    # Compute and Media Runtimes
-    apt-get install -y \
-        intel-opencl-icd intel-level-zero-gpu level-zero \
-        intel-media-va-driver-non-free libmfx1 libmfxgen1 libvpl2 \
-        libegl-mesa0 libegl1-mesa libegl1-mesa-dev libgbm1 libgl1-mesa-dev libgl1-mesa-dri \
-        libglapi-mesa libgles2-mesa-dev libglx-mesa0 libigdgmm12 libxatracker2 mesa-va-drivers \
-        mesa-vdpau-drivers mesa-vulkan-drivers va-driver-all vainfo hwinfo clinfo
-    # Development Packages
-    apt-get install -y libigc-dev intel-igc-cm libigdfcl-dev libigfxcmrt-dev level-zero-dev
-    # Install Intel Support Packages
-    if [ -n "$XPU_VERSION" ]; then
-        apt-get install -y intel-for-pytorch-gpu-dev-${XPU_VERSION}
-    else
-        apt-get install -y intel-for-pytorch-gpu-dev
-    fi
-
-    # Cleanup
-    apt-get autoclean && apt-get clean
-    rm -rf /var/lib/apt/lists/* /tmp/* /var/tmp/*
-}
-
-function install_centos() {
-    dnf install -y 'dnf-command(config-manager)'
-    dnf config-manager --add-repo \
-        https://repositories.intel.com/gpu/rhel/8.6/production/2328/unified/intel-gpu-8.6.repo
-    # To add the EPEL repository needed for DKMS
-    dnf -y install https://dl.fedoraproject.org/pub/epel/epel-release-latest-8.noarch.rpm
-        # https://dl.fedoraproject.org/pub/epel/epel-release-latest-9.noarch.rpm
-
-    # Create the YUM repository file in the /temp directory as a normal user
-    tee > /tmp/oneAPI.repo << EOF
-[oneAPI]
-name=Intel® oneAPI repository
-baseurl=https://yum.repos.intel.com/oneapi
-enabled=1
-gpgcheck=1
-repo_gpgcheck=1
-gpgkey=https://yum.repos.intel.com/intel-gpg-keys/GPG-PUB-KEY-INTEL-SW-PRODUCTS.PUB
-EOF
-
-    # Move the newly created oneAPI.repo file to the YUM configuration directory /etc/yum.repos.d
-    mv /tmp/oneAPI.repo /etc/yum.repos.d
-
-    # The xpu-smi packages
-    dnf install -y flex bison xpu-smi
-    # Compute and Media Runtimes
-    dnf install -y \
-        intel-opencl intel-media intel-mediasdk libmfxgen1 libvpl2\
-        level-zero intel-level-zero-gpu mesa-dri-drivers mesa-vulkan-drivers \
-        mesa-vdpau-drivers libdrm mesa-libEGL mesa-libgbm mesa-libGL \
-        mesa-libxatracker libvpl-tools intel-metrics-discovery \
-        intel-metrics-library intel-igc-core intel-igc-cm \
-        libva libva-utils intel-gmmlib libmetee intel-gsc intel-ocloc hwinfo clinfo
-    # Development packages
-    dnf install -y --refresh \
-        intel-igc-opencl-devel level-zero-devel intel-gsc-devel libmetee-devel \
-        level-zero-devel
-    # Install Intel® oneAPI Base Toolkit
-    dnf install intel-basekit -y
-
-    # Cleanup
-    dnf clean all
-    rm -rf /var/cache/yum
-    rm -rf /var/lib/yum/yumdb
-    rm -rf /var/lib/yum/history
-}
-
-
-# The installation depends on the base OS
-ID=$(grep -oP '(?<=^ID=).+' /etc/os-release | tr -d '"')
-case "$ID" in
-    ubuntu)
-        install_ubuntu
-    ;;
-    centos)
-        install_centos
-    ;;
-    *)
-        echo "Unable to determine OS..."
-        exit 1
-    ;;
-esac

.ci/docker/linter-cuda/Dockerfile

@@ -1,44 +0,0 @@
-ARG UBUNTU_VERSION
-
-FROM ubuntu:${UBUNTU_VERSION}
-
-ARG UBUNTU_VERSION
-
-ENV DEBIAN_FRONTEND noninteractive
-
-# Install common dependencies (so that this step can be cached separately)
-COPY ./common/install_base.sh install_base.sh
-RUN bash ./install_base.sh && rm install_base.sh
-
-# Install missing libomp-dev
-RUN apt-get update && apt-get install -y --no-install-recommends libomp-dev && apt-get autoclean && apt-get clean && rm -rf /var/lib/apt/lists/* /tmp/* /var/tmp/*
-
-# Install user
-COPY ./common/install_user.sh install_user.sh
-RUN bash ./install_user.sh && rm install_user.sh
-
-# Install conda and other packages (e.g., numpy, pytest)
-ARG ANACONDA_PYTHON_VERSION
-ARG CONDA_CMAKE
-ENV ANACONDA_PYTHON_VERSION=$ANACONDA_PYTHON_VERSION
-ENV PATH /opt/conda/envs/py_$ANACONDA_PYTHON_VERSION/bin:/opt/conda/bin:$PATH
-COPY requirements-ci.txt /opt/conda/requirements-ci.txt
-COPY ./common/install_conda.sh install_conda.sh
-COPY ./common/common_utils.sh common_utils.sh
-RUN bash ./install_conda.sh && rm install_conda.sh common_utils.sh /opt/conda/requirements-ci.txt
-
-# Install cuda and cudnn
-ARG CUDA_VERSION
-RUN wget -q https://raw.githubusercontent.com/pytorch/builder/main/common/install_cuda.sh -O install_cuda.sh
-RUN bash ./install_cuda.sh ${CUDA_VERSION} && rm install_cuda.sh
-ENV DESIRED_CUDA ${CUDA_VERSION}
-ENV PATH /usr/local/nvidia/bin:/usr/local/cuda/bin:$PATH
-
-# Note that Docker build forbids copying file outside the build context
-COPY ./common/install_linter.sh install_linter.sh
-COPY ./common/common_utils.sh common_utils.sh
-RUN bash ./install_linter.sh
-RUN rm install_linter.sh common_utils.sh
-
-USER jenkins
-CMD ["bash"]

.ci/docker/requirements-ci.txt

@@ -15,7 +15,7 @@ click
 #Pinned versions:
 #test that import:
 
-coremltools==5.0b5 ; python_version < "3.12"
+coremltools==5.0b5
 #Description: Apple framework for ML integration
 #Pinned versions: 5.0b5
 #test that import:
@@ -25,15 +25,10 @@ coremltools==5.0b5 ; python_version < "3.12"
 #Pinned versions:
 #test that import:
 
-dill==0.3.7
-#Description: dill extends pickle with serializing and de-serializing for most built-ins
-#Pinned versions: 0.3.7
-#test that import: dynamo/test_replay_record.py test_dataloader.py test_datapipe.py test_serialization.py
-
-expecttest==0.1.6
+expecttest==0.1.3
 #Description: method for writing tests where test framework auto populates
 # the expected output based on previous runs
-#Pinned versions: 0.1.6
+#Pinned versions: 0.1.3
 #test that import:
 
 flatbuffers==2.0
@@ -52,11 +47,6 @@ junitparser==2.1.1
 #Pinned versions: 2.1.1
 #test that import:
 
-lark==0.12.0
-#Description: parser
-#Pinned versions: 0.12.0
-#test that import:
-
 librosa>=0.6.2 ; python_version < "3.11"
 #Description: A python package for music and audio analysis
 #Pinned versions: >=0.6.2
@@ -72,11 +62,11 @@ librosa>=0.6.2 ; python_version < "3.11"
 #mkl-devel
 # see mkl
 
-#mock
+#mock # breaks ci/circleci: docker-pytorch-linux-xenial-py3-clang5-android-ndk-r19c
 #Description: A testing library that allows you to replace parts of your
 #system under test with mock objects
 #Pinned versions:
-#test that import: test_modules.py, test_nn.py,
+#test that import: test_module_init.py, test_modules.py, test_nn.py,
 #test_testing.py
 
 #MonkeyType # breaks pytorch-xla-linux-bionic-py3.7-clang8
@@ -85,16 +75,16 @@ librosa>=0.6.2 ; python_version < "3.11"
 #Pinned versions:
 #test that import:
 
-mypy==1.9.0
+mypy==0.960
 # Pin MyPy version because new errors are likely to appear with each release
 #Description: linter
-#Pinned versions: 1.9.0
+#Pinned versions: 0.960
 #test that import: test_typing.py, test_type_hints.py
 
-networkx==2.8.8
+networkx==2.6.3
 #Description: creation, manipulation, and study of
 #the structure, dynamics, and functions of complex networks
-#Pinned versions: 2.8.8
+#Pinned versions: 2.6.3 (latest version that works with Python 3.7+)
 #test that import: functorch
 
 #ninja
@@ -134,22 +124,9 @@ opt-einsum==3.3
 #Pinned versions: 3.3
 #test that import: test_linalg.py
 
-optree==0.11.0
-#Description: A library for tree manipulation
-#Pinned versions: 0.11.0
-#test that import: test_vmap.py, test_aotdispatch.py, test_dynamic_shapes.py,
-#test_pytree.py, test_ops.py, test_control_flow.py, test_modules.py,
-#common_utils.py, test_eager_transforms.py, test_python_dispatch.py,
-#test_expanded_weights.py, test_decomp.py, test_overrides.py, test_masked.py,
-#test_ops.py, test_prims.py, test_subclass.py, test_functionalization.py,
-#test_schema_check.py, test_profiler_tree.py, test_meta.py, test_torchxla_num_output.py,
-#test_utils.py, test_proxy_tensor.py, test_memory_profiler.py, test_view_ops.py,
-#test_pointwise_ops.py, test_dtensor_ops.py, test_torchinductor.py, test_fx.py,
-#test_fake_tensor.py, test_mps.py
-
-pillow==10.3.0
+#pillow
 #Description:  Python Imaging Library fork
-#Pinned versions: 10.3.0
+#Pinned versions:
 #test that import:
 
 protobuf==3.20.2
@@ -162,22 +139,27 @@ psutil
 #Pinned versions:
 #test that import: test_profiler.py, test_openmp.py, test_dataloader.py
 
-pytest==7.3.2
+pytest
 #Description: testing framework
 #Pinned versions:
 #test that import: test_typing.py, test_cpp_extensions_aot.py, run_test.py
 
-pytest-xdist==3.3.1
+pytest-xdist
 #Description: plugin for running pytest in parallel
 #Pinned versions:
 #test that import:
 
+pytest-shard
+#Description: plugin spliting up tests in pytest
+#Pinned versions:
+#test that import:
+
 pytest-flakefinder==1.1.0
 #Description: plugin for rerunning tests a fixed number of times in pytest
 #Pinned versions: 1.1.0
 #test that import:
 
-pytest-rerunfailures>=10.3
+pytest-rerunfailures
 #Description: plugin for rerunning failure tests in pytest
 #Pinned versions:
 #test that import:
@@ -197,7 +179,7 @@ xdoctest==1.1.0
 #Pinned versions: 1.1.0
 #test that import:
 
-pygments==2.15.0
+pygments==2.12.0
 #Description: support doctest highlighting
 #Pinned versions: 2.12.0
 #test that import: the doctests
@@ -217,8 +199,7 @@ pygments==2.15.0
 #Pinned versions: 10.9.0
 #test that import:
 
-scikit-image==0.19.3 ; python_version < "3.10"
-scikit-image==0.20.0 ; python_version >= "3.10"
+scikit-image
 #Description: image processing routines
 #Pinned versions:
 #test that import: test_nn.py
@@ -228,11 +209,12 @@ scikit-image==0.20.0 ; python_version >= "3.10"
 #Pinned versions: 0.20.3
 #test that import:
 
-scipy==1.10.1 ; python_version <= "3.11"
-scipy==1.12.0 ; python_version == "3.12"
+scipy==1.6.3 ; python_version < "3.10"
+scipy==1.8.1 ; python_version == "3.10"
+scipy==1.9.3 ; python_version == "3.11"
 # Pin SciPy because of failing distribution tests (see #60347)
 #Description: scientific python
-#Pinned versions: 1.10.1
+#Pinned versions: 1.6.3
 #test that import: test_unary_ufuncs.py, test_torch.py,test_tensor_creation_ops.py
 #test_spectral_ops.py, test_sparse_csr.py, test_reductions.py,test_nn.py
 #test_linalg.py, test_binary_ufuncs.py
@@ -242,13 +224,12 @@ scipy==1.12.0 ; python_version == "3.12"
 #Pinned versions:
 #test that import:
 
-tb-nightly==2.13.0a20230426
+tb-nightly
 #Description: TensorBoard
 #Pinned versions:
 #test that import:
 
-# needed by torchgen utils
-typing-extensions
+#typing-extensions
 #Description: type hints for python
 #Pinned versions:
 #test that import:
@@ -263,10 +244,9 @@ unittest-xml-reporting<=3.2.0,>=2.0.0
 #Pinned versions:
 #test that import:
 
-#lintrunner is supported on aarch64-linux only from 0.12.4 version
-lintrunner==0.12.5
-#Description: all about linters!
-#Pinned versions: 0.12.5
+lintrunner==0.9.2
+#Description: all about linters
+#Pinned versions: 0.9.2
 #test that import:
 
 rockset==1.0.3
@@ -274,41 +254,7 @@ rockset==1.0.3
 #Pinned versions: 1.0.3
 #test that import:
 
-ghstack==0.8.0
+ghstack==0.7.1
 #Description: ghstack tool
-#Pinned versions: 0.8.0
+#Pinned versions: 0.7.1
 #test that import:
-
-jinja2==3.1.4
-#Description: jinja2 template engine
-#Pinned versions: 3.1.4
-#test that import:
-
-pytest-cpp==2.3.0
-#Description: This is used by pytest to invoke C++ tests
-#Pinned versions: 2.3.0
-#test that import:
-
-z3-solver==4.12.2.0
-#Description: The Z3 Theorem Prover Project
-#Pinned versions:
-#test that import:
-
-tensorboard==2.13.0
-#Description: Also included in .ci/docker/requirements-docs.txt
-#Pinned versions:
-#test that import: test_tensorboard
-
-pywavelets==1.4.1 ; python_version < "3.12"
-pywavelets==1.5.0 ; python_version >= "3.12"
-#Description: This is a requirement of scikit-image, we need to pin
-# it here because 1.5.0 conflicts with numpy 1.21.2 used in CI
-#Pinned versions: 1.4.1
-#test that import:
-
-lxml==5.0.0.
-#Description: This is a requirement of unittest-xml-reporting
-
-# Python-3.9 binaries
-
-PyGithub==2.3.0

.ci/docker/requirements-docs.txt

@@ -1,49 +0,0 @@
-sphinx==5.3.0
-#Description: This is used to generate PyTorch docs
-#Pinned versions: 5.3.0
--e git+https://github.com/pytorch/pytorch_sphinx_theme.git#egg=pytorch_sphinx_theme
-
-# TODO: sphinxcontrib.katex 0.9.0 adds a local KaTeX server to speed up pre-rendering
-# but it doesn't seem to work and hangs around idly. The initial thought is probably
-# something related to Docker setup. We can investigate this later
-sphinxcontrib.katex==0.8.6
-#Description: This is used to generate PyTorch docs
-#Pinned versions: 0.8.6
-
-matplotlib==3.5.3
-#Description: This is used to generate PyTorch docs
-#Pinned versions: 3.5.3
-
-tensorboard==2.13.0
-#Description: This is used to generate PyTorch docs
-#Pinned versions: 2.13.0
-
-breathe==4.34.0
-#Description: This is used to generate PyTorch C++ docs
-#Pinned versions: 4.34.0
-
-exhale==0.2.3
-#Description: This is used to generate PyTorch C++ docs
-#Pinned versions: 0.2.3
-
-docutils==0.16
-#Description: This is used to generate PyTorch C++ docs
-#Pinned versions: 0.16
-
-bs4==0.0.1
-#Description: This is used to generate PyTorch C++ docs
-#Pinned versions: 0.0.1
-
-IPython==8.12.0
-#Description: This is used to generate PyTorch functorch docs
-#Pinned versions: 8.12.0
-
-myst-nb==0.17.2
-#Description: This is used to generate PyTorch functorch docs
-#Pinned versions: 0.13.2
-
-# The following are required to build torch.distributed.elastic.rendezvous.etcd* docs
-python-etcd==0.4.5
-sphinx-copybutton==0.5.0
-sphinx-panels==0.4.1
-myst-parser==0.18.1

.ci/docker/triton_version.txt

@@ -1 +0,0 @@
-3.0.0

.ci/docker/ubuntu-cuda/Dockerfile

@@ -56,11 +56,11 @@ RUN if [ -n "${DB}" ]; then bash ./install_db.sh; fi
 RUN rm install_db.sh
 ENV INSTALLED_DB ${DB}
 
-# (optional) Install vision packages like OpenCV
+# (optional) Install vision packages like OpenCV and ffmpeg
 ARG VISION
-COPY ./common/install_vision.sh ./common/cache_vision_models.sh ./common/common_utils.sh ./
+COPY ./common/install_vision.sh install_vision.sh
 RUN if [ -n "${VISION}" ]; then bash ./install_vision.sh; fi
-RUN rm install_vision.sh cache_vision_models.sh common_utils.sh
+RUN rm install_vision.sh
 ENV INSTALLED_VISION ${VISION}
 
 # (optional) Install UCC
@@ -79,30 +79,12 @@ ENV OPENSSL_ROOT_DIR /opt/openssl
 RUN bash ./install_openssl.sh
 ENV OPENSSL_DIR /opt/openssl
 
-ARG INDUCTOR_BENCHMARKS
-COPY ./common/install_inductor_benchmark_deps.sh install_inductor_benchmark_deps.sh
-COPY ./common/common_utils.sh common_utils.sh
-COPY ci_commit_pins/huggingface.txt huggingface.txt
-COPY ci_commit_pins/timm.txt timm.txt
-RUN if [ -n "${INDUCTOR_BENCHMARKS}" ]; then bash ./install_inductor_benchmark_deps.sh; fi
-RUN rm install_inductor_benchmark_deps.sh common_utils.sh timm.txt huggingface.txt
-
 # (optional) Install non-default CMake version
 ARG CMAKE_VERSION
 COPY ./common/install_cmake.sh install_cmake.sh
 RUN if [ -n "${CMAKE_VERSION}" ]; then bash ./install_cmake.sh; fi
 RUN rm install_cmake.sh
 
-ARG TRITON
-# Install triton, this needs to be done before sccache because the latter will
-# try to reach out to S3, which docker build runners don't have access
-COPY ./common/install_triton.sh install_triton.sh
-COPY ./common/common_utils.sh common_utils.sh
-COPY ci_commit_pins/triton.txt triton.txt
-COPY triton_version.txt triton_version.txt
-RUN if [ -n "${TRITON}" ]; then bash ./install_triton.sh; fi
-RUN rm install_triton.sh common_utils.sh triton.txt triton_version.txt
-
 # Install ccache/sccache (do this last, so we get priority in PATH)
 COPY ./common/install_cache.sh install_cache.sh
 ENV PATH /opt/cache/bin:$PATH
@@ -139,20 +121,12 @@ COPY --from=pytorch/llvm:9.0.1 /opt/llvm /opt/llvm
 ARG CUDNN_VERSION
 ARG CUDA_VERSION
 COPY ./common/install_cudnn.sh install_cudnn.sh
-RUN if [ -n "${CUDNN_VERSION}" ]; then bash install_cudnn.sh; fi
+RUN if [ "${CUDNN_VERSION}" -eq 8 ]; then bash install_cudnn.sh; fi
 RUN rm install_cudnn.sh
 
-# Install CUSPARSELT
-ARG CUDA_VERSION
-COPY ./common/install_cusparselt.sh install_cusparselt.sh
-RUN bash install_cusparselt.sh
-RUN rm install_cusparselt.sh
-
 # Delete /usr/local/cuda-11.X/cuda-11.X symlinks
 RUN if [ -h /usr/local/cuda-11.6/cuda-11.6 ]; then rm /usr/local/cuda-11.6/cuda-11.6; fi
 RUN if [ -h /usr/local/cuda-11.7/cuda-11.7 ]; then rm /usr/local/cuda-11.7/cuda-11.7; fi
-RUN if [ -h /usr/local/cuda-12.1/cuda-12.1 ]; then rm /usr/local/cuda-12.1/cuda-12.1; fi
-RUN if [ -h /usr/local/cuda-12.4/cuda-12.4 ]; then rm /usr/local/cuda-12.4/cuda-12.4; fi
 
 USER jenkins
 CMD ["bash"]

.ci/docker/ubuntu-rocm/Dockerfile

@@ -53,11 +53,11 @@ RUN if [ -n "${DB}" ]; then bash ./install_db.sh; fi
 RUN rm install_db.sh
 ENV INSTALLED_DB ${DB}
 
-# (optional) Install vision packages like OpenCV
+# (optional) Install vision packages like OpenCV and ffmpeg
 ARG VISION
-COPY ./common/install_vision.sh ./common/cache_vision_models.sh ./common/common_utils.sh ./
+COPY ./common/install_vision.sh install_vision.sh
 RUN if [ -n "${VISION}" ]; then bash ./install_vision.sh; fi
-RUN rm install_vision.sh cache_vision_models.sh common_utils.sh
+RUN rm install_vision.sh
 ENV INSTALLED_VISION ${VISION}
 
 # Install rocm
@@ -68,7 +68,6 @@ RUN rm install_rocm.sh
 COPY ./common/install_rocm_magma.sh install_rocm_magma.sh
 RUN bash ./install_rocm_magma.sh
 RUN rm install_rocm_magma.sh
-ENV ROCM_PATH /opt/rocm
 ENV PATH /opt/rocm/bin:$PATH
 ENV PATH /opt/rocm/hcc/bin:$PATH
 ENV PATH /opt/rocm/hip/bin:$PATH
@@ -78,11 +77,6 @@ ENV MAGMA_HOME /opt/rocm/magma
 ENV LANG C.UTF-8
 ENV LC_ALL C.UTF-8
 
-# Install amdsmi
-COPY ./common/install_amdsmi.sh install_amdsmi.sh
-RUN bash ./install_amdsmi.sh
-RUN rm install_amdsmi.sh
-
 # (optional) Install non-default CMake version
 ARG CMAKE_VERSION
 COPY ./common/install_cmake.sh install_cmake.sh
@@ -95,23 +89,6 @@ COPY ./common/install_ninja.sh install_ninja.sh
 RUN if [ -n "${NINJA_VERSION}" ]; then bash ./install_ninja.sh; fi
 RUN rm install_ninja.sh
 
-ARG TRITON
-# Install triton, this needs to be done before sccache because the latter will
-# try to reach out to S3, which docker build runners don't have access
-COPY ./common/install_triton.sh install_triton.sh
-COPY ./common/common_utils.sh common_utils.sh
-COPY ci_commit_pins/triton-rocm.txt triton-rocm.txt
-COPY triton_version.txt triton_version.txt
-RUN if [ -n "${TRITON}" ]; then bash ./install_triton.sh; fi
-RUN rm install_triton.sh common_utils.sh triton-rocm.txt triton_version.txt
-
-# Install AOTriton
-COPY ./aotriton_version.txt aotriton_version.txt
-COPY ./common/common_utils.sh common_utils.sh
-COPY ./common/install_aotriton.sh install_aotriton.sh
-RUN ["/bin/bash", "-c", "./install_aotriton.sh /opt/rocm && rm -rf install_aotriton.sh aotriton_version.txt common_utils.sh"]
-ENV AOTRITON_INSTALLED_PREFIX /opt/rocm/aotriton
-
 # Install ccache/sccache (do this last, so we get priority in PATH)
 COPY ./common/install_cache.sh install_cache.sh
 ENV PATH /opt/cache/bin:$PATH

.ci/docker/ubuntu-xpu/Dockerfile

@@ -1,118 +0,0 @@
-ARG UBUNTU_VERSION
-
-FROM ubuntu:${UBUNTU_VERSION}
-
-ARG UBUNTU_VERSION
-
-ENV DEBIAN_FRONTEND noninteractive
-
-ARG CLANG_VERSION
-
-# Install common dependencies (so that this step can be cached separately)
-COPY ./common/install_base.sh install_base.sh
-RUN bash ./install_base.sh && rm install_base.sh
-
-# Install clang
-ARG LLVMDEV
-COPY ./common/install_clang.sh install_clang.sh
-RUN bash ./install_clang.sh && rm install_clang.sh
-
-# Install user
-COPY ./common/install_user.sh install_user.sh
-RUN bash ./install_user.sh && rm install_user.sh
-
-# Install katex
-ARG KATEX
-COPY ./common/install_docs_reqs.sh install_docs_reqs.sh
-RUN bash ./install_docs_reqs.sh && rm install_docs_reqs.sh
-
-# Install conda and other packages (e.g., numpy, pytest)
-ARG ANACONDA_PYTHON_VERSION
-ARG CONDA_CMAKE
-ARG DOCS
-ENV ANACONDA_PYTHON_VERSION=$ANACONDA_PYTHON_VERSION
-ENV PATH /opt/conda/envs/py_$ANACONDA_PYTHON_VERSION/bin:/opt/conda/bin:$PATH
-ENV DOCS=$DOCS
-COPY requirements-ci.txt requirements-docs.txt /opt/conda/
-COPY ./common/install_conda.sh install_conda.sh
-COPY ./common/common_utils.sh common_utils.sh
-RUN bash ./install_conda.sh && rm install_conda.sh common_utils.sh /opt/conda/requirements-ci.txt /opt/conda/requirements-docs.txt
-
-# Install gcc
-ARG GCC_VERSION
-COPY ./common/install_gcc.sh install_gcc.sh
-RUN bash ./install_gcc.sh && rm install_gcc.sh
-
-# Install lcov for C++ code coverage
-COPY ./common/install_lcov.sh install_lcov.sh
-RUN  bash ./install_lcov.sh && rm install_lcov.sh
-
-COPY ./common/install_openssl.sh install_openssl.sh
-RUN bash ./install_openssl.sh
-ENV OPENSSL_ROOT_DIR /opt/openssl
-ENV OPENSSL_DIR /opt/openssl
-RUN rm install_openssl.sh
-
-ARG INDUCTOR_BENCHMARKS
-COPY ./common/install_inductor_benchmark_deps.sh install_inductor_benchmark_deps.sh
-COPY ./common/common_utils.sh common_utils.sh
-COPY ci_commit_pins/huggingface.txt huggingface.txt
-COPY ci_commit_pins/timm.txt timm.txt
-RUN if [ -n "${INDUCTOR_BENCHMARKS}" ]; then bash ./install_inductor_benchmark_deps.sh; fi
-RUN rm install_inductor_benchmark_deps.sh common_utils.sh timm.txt huggingface.txt
-
-# Install XPU Dependencies
-ARG XPU_VERSION
-COPY ./common/install_xpu.sh install_xpu.sh
-RUN bash ./install_xpu.sh && rm install_xpu.sh
-
-ARG TRITON
-# Install triton, this needs to be done before sccache because the latter will
-# try to reach out to S3, which docker build runners don't have access
-COPY ./common/install_triton.sh install_triton.sh
-COPY ./common/common_utils.sh common_utils.sh
-COPY ci_commit_pins/triton-xpu.txt triton-xpu.txt
-COPY triton_version.txt triton_version.txt
-RUN if [ -n "${TRITON}" ]; then bash ./install_triton.sh; fi
-RUN rm install_triton.sh common_utils.sh triton-xpu.txt triton_version.txt
-
-# (optional) Install database packages like LMDB and LevelDB
-ARG DB
-COPY ./common/install_db.sh install_db.sh
-RUN if [ -n "${DB}" ]; then bash ./install_db.sh; fi
-RUN rm install_db.sh
-ENV INSTALLED_DB ${DB}
-
-# (optional) Install vision packages like OpenCV
-ARG VISION
-COPY ./common/install_vision.sh ./common/cache_vision_models.sh ./common/common_utils.sh ./
-RUN if [ -n "${VISION}" ]; then bash ./install_vision.sh; fi
-RUN rm install_vision.sh cache_vision_models.sh common_utils.sh
-ENV INSTALLED_VISION ${VISION}
-
-# (optional) Install non-default CMake version
-ARG CMAKE_VERSION
-COPY ./common/install_cmake.sh install_cmake.sh
-RUN if [ -n "${CMAKE_VERSION}" ]; then bash ./install_cmake.sh; fi
-RUN rm install_cmake.sh
-
-# (optional) Install non-default Ninja version
-ARG NINJA_VERSION
-COPY ./common/install_ninja.sh install_ninja.sh
-RUN if [ -n "${NINJA_VERSION}" ]; then bash ./install_ninja.sh; fi
-RUN rm install_ninja.sh
-
-# Install ccache/sccache (do this last, so we get priority in PATH)
-COPY ./common/install_cache.sh install_cache.sh
-ENV PATH /opt/cache/bin:$PATH
-RUN bash ./install_cache.sh && rm install_cache.sh
-
-# Include BUILD_ENVIRONMENT environment variable in image
-ARG BUILD_ENVIRONMENT
-ENV BUILD_ENVIRONMENT ${BUILD_ENVIRONMENT}
-
-# Install LLVM dev version (Defined in the pytorch/builder github repository)
-COPY --from=pytorch/llvm:9.0.1 /opt/llvm /opt/llvm
-
-USER jenkins
-CMD ["bash"]

.ci/docker/ubuntu/Dockerfile

@@ -17,6 +17,13 @@ ARG LLVMDEV
 COPY ./common/install_clang.sh install_clang.sh
 RUN bash ./install_clang.sh && rm install_clang.sh
 
+# (optional) Install thrift.
+ARG THRIFT
+COPY ./common/install_thrift.sh install_thrift.sh
+RUN if [ -n "${THRIFT}" ]; then bash ./install_thrift.sh; fi
+RUN rm install_thrift.sh
+ENV INSTALLED_THRIFT ${THRIFT}
+
 # Install user
 COPY ./common/install_user.sh install_user.sh
 RUN bash ./install_user.sh && rm install_user.sh
@@ -29,15 +36,12 @@ RUN bash ./install_docs_reqs.sh && rm install_docs_reqs.sh
 # Install conda and other packages (e.g., numpy, pytest)
 ARG ANACONDA_PYTHON_VERSION
 ARG CONDA_CMAKE
-ARG DOCS
 ENV ANACONDA_PYTHON_VERSION=$ANACONDA_PYTHON_VERSION
 ENV PATH /opt/conda/envs/py_$ANACONDA_PYTHON_VERSION/bin:/opt/conda/bin:$PATH
-ENV DOCS=$DOCS
-COPY requirements-ci.txt requirements-docs.txt /opt/conda/
+COPY requirements-ci.txt /opt/conda/requirements-ci.txt
 COPY ./common/install_conda.sh install_conda.sh
 COPY ./common/common_utils.sh common_utils.sh
-RUN bash ./install_conda.sh && rm install_conda.sh common_utils.sh /opt/conda/requirements-ci.txt /opt/conda/requirements-docs.txt
-RUN if [ -n "${UNINSTALL_DILL}" ]; then pip uninstall -y dill; fi
+RUN bash ./install_conda.sh && rm install_conda.sh common_utils.sh /opt/conda/requirements-ci.txt
 
 # Install gcc
 ARG GCC_VERSION
@@ -80,22 +84,22 @@ RUN if [ -n "${DB}" ]; then bash ./install_db.sh; fi
 RUN rm install_db.sh
 ENV INSTALLED_DB ${DB}
 
-# (optional) Install vision packages like OpenCV
+# (optional) Install vision packages like OpenCV and ffmpeg
 ARG VISION
-COPY ./common/install_vision.sh ./common/cache_vision_models.sh ./common/common_utils.sh ./
+COPY ./common/install_vision.sh install_vision.sh
 RUN if [ -n "${VISION}" ]; then bash ./install_vision.sh; fi
-RUN rm install_vision.sh cache_vision_models.sh common_utils.sh
+RUN rm install_vision.sh
 ENV INSTALLED_VISION ${VISION}
 
 # (optional) Install Android NDK
 ARG ANDROID
 ARG ANDROID_NDK
 ARG GRADLE_VERSION
-COPY ./common/install_android.sh ./common/cache_vision_models.sh ./common/common_utils.sh ./
+COPY ./common/install_android.sh install_android.sh
 COPY ./android/AndroidManifest.xml AndroidManifest.xml
 COPY ./android/build.gradle build.gradle
 RUN if [ -n "${ANDROID}" ]; then bash ./install_android.sh; fi
-RUN rm install_android.sh cache_vision_models.sh common_utils.sh
+RUN rm install_android.sh
 RUN rm AndroidManifest.xml
 RUN rm build.gradle
 ENV INSTALLED_ANDROID ${ANDROID}
@@ -130,50 +134,10 @@ ENV OPENSSL_ROOT_DIR /opt/openssl
 ENV OPENSSL_DIR /opt/openssl
 RUN rm install_openssl.sh
 
-ARG INDUCTOR_BENCHMARKS
-COPY ./common/install_inductor_benchmark_deps.sh install_inductor_benchmark_deps.sh
-COPY ./common/common_utils.sh common_utils.sh
-COPY ci_commit_pins/huggingface.txt huggingface.txt
-COPY ci_commit_pins/timm.txt timm.txt
-RUN if [ -n "${INDUCTOR_BENCHMARKS}" ]; then bash ./install_inductor_benchmark_deps.sh; fi
-RUN rm install_inductor_benchmark_deps.sh common_utils.sh timm.txt huggingface.txt
-
-ARG TRITON
-# Install triton, this needs to be done before sccache because the latter will
-# try to reach out to S3, which docker build runners don't have access
-COPY ./common/install_triton.sh install_triton.sh
-COPY ./common/common_utils.sh common_utils.sh
-COPY ci_commit_pins/triton.txt triton.txt
-RUN if [ -n "${TRITON}" ]; then bash ./install_triton.sh; fi
-RUN rm install_triton.sh common_utils.sh triton.txt
-
-ARG EXECUTORCH
-# Build and install executorch
-COPY ./common/install_executorch.sh install_executorch.sh
-COPY ./common/common_utils.sh common_utils.sh
-COPY ci_commit_pins/executorch.txt executorch.txt
-RUN if [ -n "${EXECUTORCH}" ]; then bash ./install_executorch.sh; fi
-RUN rm install_executorch.sh common_utils.sh executorch.txt
-
-ARG ONNX
-# Install ONNX dependencies
-COPY ./common/install_onnx.sh ./common/common_utils.sh ./
-RUN if [ -n "${ONNX}" ]; then bash ./install_onnx.sh; fi
-RUN rm install_onnx.sh common_utils.sh
-
-# (optional) Build ACL
-ARG ACL
-COPY ./common/install_acl.sh install_acl.sh
-RUN if [ -n "${ACL}" ]; then bash ./install_acl.sh; fi
-RUN rm install_acl.sh
-ENV INSTALLED_ACL ${ACL}
-
 # Install ccache/sccache (do this last, so we get priority in PATH)
-ARG SKIP_SCCACHE_INSTALL
 COPY ./common/install_cache.sh install_cache.sh
 ENV PATH /opt/cache/bin:$PATH
-RUN if [ -z "${SKIP_SCCACHE_INSTALL}" ]; then bash ./install_cache.sh; fi
-RUN rm install_cache.sh
+RUN bash ./install_cache.sh && rm install_cache.sh
 
 # Add jni.h for java host build
 COPY ./common/install_jni.sh install_jni.sh
@@ -190,9 +154,7 @@ ARG BUILD_ENVIRONMENT
 ENV BUILD_ENVIRONMENT ${BUILD_ENVIRONMENT}
 
 # Install LLVM dev version (Defined in the pytorch/builder github repository)
-ARG SKIP_LLVM_SRC_BUILD_INSTALL
 COPY --from=pytorch/llvm:9.0.1 /opt/llvm /opt/llvm
-RUN if [ -n "${SKIP_LLVM_SRC_BUILD_INSTALL}" ]; then set -eu; rm -rf /opt/llvm; fi
 
 # AWS specific CUDA build guidance
 ENV TORCH_CUDA_ARCH_LIST Maxwell

.ci/onnx/common.sh

@@ -1,9 +1,5 @@
-#!/bin/bash
-
 set -ex
 
-source "$(dirname "${BASH_SOURCE[0]}")/../pytorch/common_utils.sh"
-
 LOCAL_DIR=$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)
 ROOT_DIR=$(cd "$LOCAL_DIR"/../.. && pwd)
 TEST_DIR="$ROOT_DIR/test"

.ci/onnx/test.sh

@@ -3,27 +3,72 @@
 # shellcheck source=./common.sh
 source "$(dirname "${BASH_SOURCE[0]}")/common.sh"
 
-# Workaround for dind-rootless userid mapping (https://github.com/pytorch/ci-infra/issues/96)
-WORKSPACE_ORIGINAL_OWNER_ID=$(stat -c '%u' "/var/lib/jenkins/workspace")
-cleanup_workspace() {
-  echo "sudo may print the following warning message that can be ignored. The chown command will still run."
-  echo "    sudo: setrlimit(RLIMIT_STACK): Operation not permitted"
-  echo "For more details refer to https://github.com/sudo-project/sudo/issues/42"
-  sudo chown -R "$WORKSPACE_ORIGINAL_OWNER_ID" /var/lib/jenkins/workspace
-}
-# Disable shellcheck SC2064 as we want to parse the original owner immediately.
-# shellcheck disable=SC2064
-trap_add cleanup_workspace EXIT
-sudo chown -R jenkins /var/lib/jenkins/workspace
-git config --global --add safe.directory /var/lib/jenkins/workspace
+if [[ ${BUILD_ENVIRONMENT} == *onnx* ]]; then
+  pip install click mock tabulate networkx==2.0
+  pip -q install --user "file:///var/lib/jenkins/workspace/third_party/onnx#egg=onnx"
+fi
 
+# Skip tests in environments where they are not built/applicable
+if [[ "${BUILD_ENVIRONMENT}" == *-android* ]]; then
+  echo 'Skipping tests'
+  exit 0
+fi
+if [[ "${BUILD_ENVIRONMENT}" == *-rocm* ]]; then
+  # temporary to locate some kernel issues on the CI nodes
+  export HSAKMT_DEBUG_LEVEL=4
+fi
+# These additional packages are needed for circleci ROCm builds.
+if [[ $BUILD_ENVIRONMENT == *rocm* ]]; then
+    # Need networkx 2.0 because bellmand_ford was moved in 2.1 . Scikit-image by
+    # defaults installs the most recent networkx version, so we install this lower
+    # version explicitly before scikit-image pulls it in as a dependency
+    pip install networkx==2.0
+    # click - onnx
+    pip install --progress-bar off click protobuf tabulate virtualenv mock typing-extensions
+fi
+
+################################################################################
+# Python tests #
+################################################################################
+if [[ "$BUILD_ENVIRONMENT" == *cmake* ]]; then
+  exit 0
+fi
+
+# If pip is installed as root, we must use sudo.
+# CircleCI docker images could install conda as jenkins user, or use the OS's python package.
+PIP=$(which pip)
+PIP_USER=$(stat --format '%U' $PIP)
+CURRENT_USER=$(id -u -n)
+if [[ "$PIP_USER" = root && "$CURRENT_USER" != root ]]; then
+  MAYBE_SUDO=sudo
+fi
+
+# Uninstall pre-installed hypothesis and coverage to use an older version as newer
+# versions remove the timeout parameter from settings which ideep/conv_transpose_test.py uses
+$MAYBE_SUDO pip -q uninstall -y hypothesis
+$MAYBE_SUDO pip -q uninstall -y coverage
+
+# "pip install hypothesis==3.44.6" from official server is unreliable on
+# CircleCI, so we host a copy on S3 instead
+$MAYBE_SUDO pip -q install attrs==18.1.0 -f https://s3.amazonaws.com/ossci-linux/wheels/attrs-18.1.0-py2.py3-none-any.whl
+$MAYBE_SUDO pip -q install coverage==4.5.1 -f https://s3.amazonaws.com/ossci-linux/wheels/coverage-4.5.1-cp36-cp36m-macosx_10_12_x86_64.whl
+$MAYBE_SUDO pip -q install hypothesis==4.57.1
+
+##############
+# ONNX tests #
+##############
 if [[ "$BUILD_ENVIRONMENT" == *onnx* ]]; then
-  # TODO: This can be removed later once vision is also part of the Docker image
   pip install -q --user --no-use-pep517 "git+https://github.com/pytorch/vision.git@$(cat .github/ci_commit_pins/vision.txt)"
+  pip install -q --user transformers==4.25.1
+  pip install -q --user ninja flatbuffers==2.0 numpy==1.22.4 onnxruntime==1.14.0 beartype==0.10.4
+  # TODO: change this when onnx 1.13.1 is released.
+  pip install --no-use-pep517 'onnx @ git+https://github.com/onnx/onnx@e192ba01e438d22ca2dedd7956e28e3551626c91'
+  # TODO: change this when onnx-script is on testPypi
+  pip install 'onnx-script @ git+https://github.com/microsoft/onnx-script@a71e35bcd72537bf7572536ee57250a0c0488bf6'
+  # numba requires numpy <= 1.20, onnxruntime requires numpy >= 1.21.
+  # We don't actually need it for our tests, but it's imported if it's present, so uninstall.
+  pip uninstall -q --yes numba
   # JIT C++ extensions require ninja, so put it into PATH.
   export PATH="/var/lib/jenkins/.local/bin:$PATH"
-  # NB: ONNX test is fast (~15m) so it's ok to retry it few more times to avoid any flaky issue, we
-  # need to bring this to the standard PyTorch run_test eventually. The issue will be tracked in
-  # https://github.com/pytorch/pytorch/issues/98626
   "$ROOT_DIR/scripts/onnx/test.sh"
 fi

.ci/pytorch/build-asan.sh

@@ -0,0 +1,42 @@
+#!/bin/bash
+
+# Required environment variable: $BUILD_ENVIRONMENT
+# (This is set by default in the Docker images we build, so you don't
+# need to set it yourself.
+
+# shellcheck source=./common.sh
+source "$(dirname "${BASH_SOURCE[0]}")/common.sh"
+# shellcheck source=./common-build.sh
+source "$(dirname "${BASH_SOURCE[0]}")/common-build.sh"
+
+echo "Clang version:"
+clang --version
+
+python tools/stats/export_test_times.py
+
+# detect_leaks=0: Python is very leaky, so we need suppress it
+# symbolize=1: Gives us much better errors when things go wrong
+export ASAN_OPTIONS=detect_leaks=0:detect_stack_use_after_return=1:symbolize=1:detect_odr_violation=0
+if [ -n "$(which conda)" ]; then
+  export CMAKE_PREFIX_PATH=/opt/conda
+fi
+
+# TODO: Make the ASAN flags a centralized env var and unify with USE_ASAN option
+CC="clang" CXX="clang++" LDSHARED="clang --shared" \
+  CFLAGS="-fsanitize=address -fsanitize=undefined -fno-sanitize-recover=all -fsanitize-address-use-after-scope -shared-libasan" \
+  USE_ASAN=1 USE_CUDA=0 USE_MKLDNN=0 \
+  python setup.py bdist_wheel
+  pip_install_whl "$(echo dist/*.whl)"
+
+# Test building via the sdist source tarball
+python setup.py sdist
+mkdir -p /tmp/tmp
+pushd /tmp/tmp
+tar zxf "$(dirname "${BASH_SOURCE[0]}")/../../dist/"*.tar.gz
+cd torch-*
+python setup.py build --cmake-only
+popd
+
+print_sccache_stats
+
+assert_git_not_dirty

.ci/pytorch/build-tsan.sh

@@ -0,0 +1,29 @@
+#!/bin/bash
+
+# Required environment variable: $BUILD_ENVIRONMENT
+# (This is set by default in the Docker images we build, so you don't
+# need to set it yourself.
+
+# shellcheck source=./common.sh
+source "$(dirname "${BASH_SOURCE[0]}")/common.sh"
+# shellcheck source=./common-build.sh
+source "$(dirname "${BASH_SOURCE[0]}")/common-build.sh"
+
+echo "Clang version:"
+clang --version
+
+python tools/stats/export_test_times.py
+
+if [ -n "$(which conda)" ]; then
+  export CMAKE_PREFIX_PATH=/opt/conda
+fi
+
+CC="clang" CXX="clang++" LDSHARED="clang --shared" \
+  CFLAGS="-fsanitize=thread" \
+  USE_TSAN=1 USE_CUDA=0 USE_MKLDNN=0 \
+  python setup.py bdist_wheel
+  pip_install_whl "$(echo dist/*.whl)"
+
+print_sccache_stats
+
+assert_git_not_dirty

.ci/pytorch/build.sh

@@ -11,6 +11,14 @@ source "$(dirname "${BASH_SOURCE[0]}")/common.sh"
 # shellcheck source=./common-build.sh
 source "$(dirname "${BASH_SOURCE[0]}")/common-build.sh"
 
+if [[ "$BUILD_ENVIRONMENT" == *-clang7-asan* ]]; then
+  exec "$(dirname "${BASH_SOURCE[0]}")/build-asan.sh" "$@"
+fi
+
+if [[ "$BUILD_ENVIRONMENT" == *-clang7-tsan* ]]; then
+  exec "$(dirname "${BASH_SOURCE[0]}")/build-tsan.sh" "$@"
+fi
+
 if [[ "$BUILD_ENVIRONMENT" == *-mobile-*build* ]]; then
   exec "$(dirname "${BASH_SOURCE[0]}")/build-mobile.sh" "$@"
 fi
@@ -28,8 +36,6 @@ echo "Environment variables:"
 env
 
 if [[ "$BUILD_ENVIRONMENT" == *cuda* ]]; then
-  # Use jemalloc during compilation to mitigate https://github.com/pytorch/pytorch/issues/116289
-  export LD_PRELOAD=/usr/lib/x86_64-linux-gnu/libjemalloc.so.2
   echo "NVCC version:"
   nvcc --version
 fi
@@ -38,13 +44,20 @@ if [[ "$BUILD_ENVIRONMENT" == *cuda11* ]]; then
   if [[ "$BUILD_ENVIRONMENT" != *cuda11.3* && "$BUILD_ENVIRONMENT" != *clang* ]]; then
     # TODO: there is a linking issue when building with UCC using clang,
     # disable it for now and to be fix later.
-    # TODO: disable UCC temporarily to enable CUDA 12.1 in CI
     export USE_UCC=1
     export USE_SYSTEM_UCC=1
   fi
 fi
 
-if [[ ${BUILD_ENVIRONMENT} == *"parallelnative"* ]]; then
+if [[ ${BUILD_ENVIRONMENT} == *"caffe2"* ]]; then
+  echo "Caffe2 build is ON"
+  export BUILD_CAFFE2=ON
+fi
+
+if [[ ${BUILD_ENVIRONMENT} == *"paralleltbb"* ]]; then
+  export ATEN_THREADING=TBB
+  export USE_TBB=1
+elif [[ ${BUILD_ENVIRONMENT} == *"parallelnative"* ]]; then
   export ATEN_THREADING=NATIVE
 fi
 
@@ -57,12 +70,6 @@ else
   export LLVM_DIR=/opt/llvm/lib/cmake/llvm
 fi
 
-if [[ "$BUILD_ENVIRONMENT" == *executorch* ]]; then
-  # To build test_edge_op_registration
-  export BUILD_EXECUTORCH=ON
-  export USE_CUDA=0
-fi
-
 if ! which conda; then
   # In ROCm CIs, we are doing cross compilation on build machines with
   # intel cpu and later run tests on machines with amd cpu.
@@ -73,35 +80,7 @@ if ! which conda; then
     export USE_MKLDNN=0
   fi
 else
-  # CMAKE_PREFIX_PATH precedences
-  # 1. $CONDA_PREFIX, if defined. This follows the pytorch official build instructions.
-  # 2. /opt/conda/envs/py_${ANACONDA_PYTHON_VERSION}, if ANACONDA_PYTHON_VERSION defined.
-  #    This is for CI, which defines ANACONDA_PYTHON_VERSION but not CONDA_PREFIX.
-  # 3. $(conda info --base). The fallback value of pytorch official build
-  #    instructions actually refers to this.
-  #    Commonly this is /opt/conda/
-  if [[ -v CONDA_PREFIX ]]; then
-    export CMAKE_PREFIX_PATH=${CONDA_PREFIX}
-  elif [[ -v ANACONDA_PYTHON_VERSION ]]; then
-    export CMAKE_PREFIX_PATH="/opt/conda/envs/py_${ANACONDA_PYTHON_VERSION}"
-  else
-    # already checked by `! which conda`
-    CMAKE_PREFIX_PATH="$(conda info --base)"
-    export CMAKE_PREFIX_PATH
-  fi
-
-  # Workaround required for MKL library linkage
-  # https://github.com/pytorch/pytorch/issues/119557
-  if [ "$ANACONDA_PYTHON_VERSION" = "3.12" ]; then
-    export CMAKE_LIBRARY_PATH="/opt/conda/envs/py_$ANACONDA_PYTHON_VERSION/lib/"
-    export CMAKE_INCLUDE_PATH="/opt/conda/envs/py_$ANACONDA_PYTHON_VERSION/include/"
-  fi
-fi
-
-if [[ "$BUILD_ENVIRONMENT" == *aarch64* ]]; then
-  export USE_MKLDNN=1
-  export USE_MKLDNN_ACL=1
-  export ACL_ROOT_DIR=/ComputeLibrary
+  export CMAKE_PREFIX_PATH=/opt/conda
 fi
 
 if [[ "$BUILD_ENVIRONMENT" == *libtorch* ]]; then
@@ -173,12 +152,6 @@ if [[ "$BUILD_ENVIRONMENT" == *rocm* ]]; then
   python tools/amd_build/build_amd.py
 fi
 
-if [[ "$BUILD_ENVIRONMENT" == *xpu* ]]; then
-  # shellcheck disable=SC1091
-  source /opt/intel/oneapi/compiler/latest/env/vars.sh
-  export USE_XPU=1
-fi
-
 # sccache will fail for CUDA builds if all cores are used for compiling
 # gcc 7 with sccache seems to have intermittent OOM issue if all cores are used
 if [ -z "$MAX_JOBS" ]; then
@@ -193,27 +166,11 @@ if [[ "$BUILD_ENVIRONMENT" == *cuda* && -z "$TORCH_CUDA_ARCH_LIST" ]]; then
   exit 1
 fi
 
-# We only build FlashAttention files for CUDA 8.0+, and they require large amounts of
-# memory to build and will OOM
-if [[ "$BUILD_ENVIRONMENT" == *cuda* ]] && [[ "$TORCH_CUDA_ARCH_LIST" == *"8.6"* || "$TORCH_CUDA_ARCH_LIST" == *"8.0"* ]]; then
-  echo "WARNING: FlashAttention files require large amounts of memory to build and will OOM"
-  echo "Setting MAX_JOBS=(nproc-2)/3 to reduce memory usage"
-  export MAX_JOBS="$(( $(nproc --ignore=2) / 3 ))"
-fi
-
 if [[ "${BUILD_ENVIRONMENT}" == *clang* ]]; then
   export CC=clang
   export CXX=clang++
 fi
 
-if [[ "$BUILD_ENVIRONMENT" == *-clang*-asan* ]]; then
-  export LDSHARED="clang --shared"
-  export USE_CUDA=0
-  export USE_ASAN=1
-  export UBSAN_FLAGS="-fno-sanitize-recover=all;-fno-sanitize=float-divide-by-zero;-fno-sanitize=float-cast-overflow"
-  unset USE_LLVM
-fi
-
 if [[ "${BUILD_ENVIRONMENT}" == *no-ops* ]]; then
   export USE_PER_OPERATOR_HEADERS=0
 fi
@@ -230,41 +187,20 @@ if [[ "${BUILD_ENVIRONMENT}" != *android* && "${BUILD_ENVIRONMENT}" != *cuda* ]]
   export BUILD_STATIC_RUNTIME_BENCHMARK=ON
 fi
 
-# Do not change workspace permissions for ROCm CI jobs
-# as it can leave workspace with bad permissions for cancelled jobs
-if [[ "$BUILD_ENVIRONMENT" != *rocm* ]]; then
-  # Workaround for dind-rootless userid mapping (https://github.com/pytorch/ci-infra/issues/96)
-  WORKSPACE_ORIGINAL_OWNER_ID=$(stat -c '%u' "/var/lib/jenkins/workspace")
-  cleanup_workspace() {
-    echo "sudo may print the following warning message that can be ignored. The chown command will still run."
-    echo "    sudo: setrlimit(RLIMIT_STACK): Operation not permitted"
-    echo "For more details refer to https://github.com/sudo-project/sudo/issues/42"
-    sudo chown -R "$WORKSPACE_ORIGINAL_OWNER_ID" /var/lib/jenkins/workspace
-  }
-  # Disable shellcheck SC2064 as we want to parse the original owner immediately.
-  # shellcheck disable=SC2064
-  trap_add cleanup_workspace EXIT
-  sudo chown -R jenkins /var/lib/jenkins/workspace
-  git config --global --add safe.directory /var/lib/jenkins/workspace
-fi
-
 if [[ "$BUILD_ENVIRONMENT" == *-bazel-* ]]; then
   set -e
 
   get_bazel
-  install_sccache_nvcc_for_bazel
 
   # Leave 1 CPU free and use only up to 80% of memory to reduce the change of crashing
   # the runner
   BAZEL_MEM_LIMIT="--local_ram_resources=HOST_RAM*.8"
   BAZEL_CPU_LIMIT="--local_cpu_resources=HOST_CPUS-1"
 
-  if [[ "$CUDA_VERSION" == "cpu" ]]; then
-    # Build torch, the Python module, and tests for CPU-only
-    tools/bazel build --config=no-tty "${BAZEL_MEM_LIMIT}" "${BAZEL_CPU_LIMIT}" --config=cpu-only :torch :torch/_C.so :all_tests
-  else
-    tools/bazel build --config=no-tty "${BAZEL_MEM_LIMIT}" "${BAZEL_CPU_LIMIT}" //...
-  fi
+  tools/bazel build --config=no-tty "${BAZEL_MEM_LIMIT}" "${BAZEL_CPU_LIMIT}" //...
+  # Build torch, the Python module, and tests for CPU-only
+  tools/bazel build --config=no-tty "${BAZEL_MEM_LIMIT}" "${BAZEL_CPU_LIMIT}" --config=cpu-only :torch :_C.so :all_tests
+
 else
   # check that setup.py would fail with bad arguments
   echo "The next three invocations are expected to fail with invalid command error messages."
@@ -273,22 +209,15 @@ else
   ( ! get_exit_code python setup.py clean bad_argument )
 
   if [[ "$BUILD_ENVIRONMENT" != *libtorch* ]]; then
+
     # rocm builds fail when WERROR=1
     # XLA test build fails when WERROR=1
     # set only when building other architectures
     # or building non-XLA tests.
     if [[ "$BUILD_ENVIRONMENT" != *rocm*  &&
           "$BUILD_ENVIRONMENT" != *xla* ]]; then
-      if [[ "$BUILD_ENVIRONMENT" != *py3.8* ]]; then
-        # Install numpy-2.0 release candidate for builds
-        # Which should be backward compatible with Numpy-1.X
-        python -mpip install --pre numpy==2.0.0rc1
-      fi
       WERROR=1 python setup.py bdist_wheel
     else
-      if [[ "$BUILD_ENVIRONMENT" == *xla* ]]; then
-        source .ci/pytorch/install_cache_xla.sh
-      fi
       python setup.py bdist_wheel
     fi
     pip_install_whl "$(echo dist/*.whl)"
@@ -330,7 +259,7 @@ else
     SITE_PACKAGES="$(python -c 'from distutils.sysconfig import get_python_lib; print(get_python_lib())')"
     mkdir -p "$CUSTOM_OP_BUILD"
     pushd "$CUSTOM_OP_BUILD"
-    cmake "$CUSTOM_OP_TEST" -DCMAKE_PREFIX_PATH="$SITE_PACKAGES/torch" -DPython_EXECUTABLE="$(which python)" \
+    cmake "$CUSTOM_OP_TEST" -DCMAKE_PREFIX_PATH="$SITE_PACKAGES/torch" -DPYTHON_EXECUTABLE="$(which python)" \
           -DCMAKE_MODULE_PATH="$CUSTOM_TEST_MODULE_PATH" -DUSE_ROCM="$CUSTOM_TEST_USE_ROCM"
     make VERBOSE=1
     popd
@@ -343,7 +272,7 @@ else
     SITE_PACKAGES="$(python -c 'from distutils.sysconfig import get_python_lib; print(get_python_lib())')"
     mkdir -p "$JIT_HOOK_BUILD"
     pushd "$JIT_HOOK_BUILD"
-    cmake "$JIT_HOOK_TEST" -DCMAKE_PREFIX_PATH="$SITE_PACKAGES/torch" -DPython_EXECUTABLE="$(which python)" \
+    cmake "$JIT_HOOK_TEST" -DCMAKE_PREFIX_PATH="$SITE_PACKAGES/torch" -DPYTHON_EXECUTABLE="$(which python)" \
           -DCMAKE_MODULE_PATH="$CUSTOM_TEST_MODULE_PATH" -DUSE_ROCM="$CUSTOM_TEST_USE_ROCM"
     make VERBOSE=1
     popd
@@ -355,7 +284,7 @@ else
     python --version
     mkdir -p "$CUSTOM_BACKEND_BUILD"
     pushd "$CUSTOM_BACKEND_BUILD"
-    cmake "$CUSTOM_BACKEND_TEST" -DCMAKE_PREFIX_PATH="$SITE_PACKAGES/torch" -DPython_EXECUTABLE="$(which python)" \
+    cmake "$CUSTOM_BACKEND_TEST" -DCMAKE_PREFIX_PATH="$SITE_PACKAGES/torch" -DPYTHON_EXECUTABLE="$(which python)" \
           -DCMAKE_MODULE_PATH="$CUSTOM_TEST_MODULE_PATH" -DUSE_ROCM="$CUSTOM_TEST_USE_ROCM"
     make VERBOSE=1
     popd
@@ -386,8 +315,4 @@ if [[ "$BUILD_ENVIRONMENT" != *libtorch* && "$BUILD_ENVIRONMENT" != *bazel* ]];
   python tools/stats/export_test_times.py
 fi
 
-# snadampal: skipping it till sccache support added for aarch64
-# https://github.com/pytorch/pytorch/issues/121559
-if [[ "$BUILD_ENVIRONMENT" != *aarch64* ]]; then
-  print_sccache_stats
-fi
+print_sccache_stats

.ci/pytorch/common-build.sh

@@ -31,7 +31,7 @@ if [[ "$BUILD_ENVIRONMENT" != *win-* ]]; then
             # as though sccache still gets used even when the sscache server isn't started
             # explicitly
             echo "Skipping sccache server initialization, setting environment variables"
-            export SCCACHE_IDLE_TIMEOUT=0
+            export SCCACHE_IDLE_TIMEOUT=1200
             export SCCACHE_ERROR_LOG=~/sccache_error.log
             export RUST_LOG=sccache::server=error
         elif [[ "${BUILD_ENVIRONMENT}" == *rocm* ]]; then
@@ -39,12 +39,11 @@ if [[ "$BUILD_ENVIRONMENT" != *win-* ]]; then
         else
             # increasing SCCACHE_IDLE_TIMEOUT so that extension_backend_test.cpp can build after this PR:
             # https://github.com/pytorch/pytorch/pull/16645
-            SCCACHE_ERROR_LOG=~/sccache_error.log SCCACHE_IDLE_TIMEOUT=0 RUST_LOG=sccache::server=error sccache --start-server
+            SCCACHE_ERROR_LOG=~/sccache_error.log SCCACHE_IDLE_TIMEOUT=1200 RUST_LOG=sccache::server=error sccache --start-server
         fi
 
-        # Report sccache stats for easier debugging. It's ok if this commands
-        # timeouts and fails on MacOS
-        sccache --zero-stats || true
+        # Report sccache stats for easier debugging
+        sccache --zero-stats
     fi
 
     if which ccache > /dev/null; then

.ci/pytorch/common.sh

@@ -22,3 +22,7 @@ fi
 # TODO: Renable libtorch testing for MacOS, see https://github.com/pytorch/pytorch/issues/62598
 # shellcheck disable=SC2034
 BUILD_TEST_LIBTORCH=0
+
+retry () {
+  "$@"  || (sleep 1 && "$@") || (sleep 2 && "$@")
+}

.ci/pytorch/common_utils.sh

@@ -43,7 +43,7 @@ function assert_git_not_dirty() {
     # TODO: we should add an option to `build_amd.py` that reverts the repo to
     #       an unmodified state.
     if [[ "$BUILD_ENVIRONMENT" != *rocm* ]] && [[ "$BUILD_ENVIRONMENT" != *xla* ]] ; then
-        git_status=$(git status --porcelain | grep -v '?? third_party' || true)
+        git_status=$(git status --porcelain)
         if [[ $git_status ]]; then
             echo "Build left local git repository checkout dirty"
             echo "git status --porcelain:"
@@ -80,34 +80,19 @@ function get_exit_code() {
 }
 
 function get_bazel() {
-  # Download and use the cross-platform, dependency-free Python
-  # version of Bazelisk to fetch the platform specific version of
-  # Bazel to use from .bazelversion.
-  retry curl --location --output tools/bazel \
-    https://raw.githubusercontent.com/bazelbuild/bazelisk/v1.16.0/bazelisk.py
-  shasum --algorithm=1 --check \
-    <(echo 'd4369c3d293814d3188019c9f7527a948972d9f8  tools/bazel')
-  chmod u+x tools/bazel
-}
+  if [[ $(uname) == "Darwin" ]]; then
+    # download bazel version
+    retry curl https://github.com/bazelbuild/bazel/releases/download/4.2.1/bazel-4.2.1-darwin-x86_64  -Lo tools/bazel
+    # verify content
+    echo '74d93848f0c9d592e341e48341c53c87e3cb304a54a2a1ee9cff3df422f0b23c  tools/bazel' | shasum -a 256 -c >/dev/null
+  else
+    # download bazel version
+    retry curl https://ossci-linux.s3.amazonaws.com/bazel-4.2.1-linux-x86_64 -o tools/bazel
+    # verify content
+    echo '1a4f3a3ce292307bceeb44f459883859c793436d564b95319aacb8af1f20557c  tools/bazel' | shasum -a 256 -c >/dev/null
+  fi
 
-# This function is bazel specific because of the bug
-# in the bazel that requires some special paths massaging
-# as a workaround. See
-# https://github.com/bazelbuild/bazel/issues/10167
-function install_sccache_nvcc_for_bazel() {
-  sudo mv /usr/local/cuda/bin/nvcc /usr/local/cuda/bin/nvcc-real
-
-  # Write the `/usr/local/cuda/bin/nvcc`
-  cat << EOF | sudo tee /usr/local/cuda/bin/nvcc
-#!/bin/sh
-if [ \$(env -u LD_PRELOAD ps -p \$PPID -o comm=) != sccache ]; then
-  exec sccache /usr/local/cuda/bin/nvcc "\$@"
-else
-  exec external/local_cuda/cuda/bin/nvcc-real "\$@"
-fi
-EOF
-
-  sudo chmod +x /usr/local/cuda/bin/nvcc
+  chmod +x tools/bazel
 }
 
 function install_monkeytype {
@@ -120,65 +105,21 @@ function get_pinned_commit() {
   cat .github/ci_commit_pins/"${1}".txt
 }
 
-function install_torchaudio() {
-  local commit
-  commit=$(get_pinned_commit audio)
-  if [[ "$1" == "cuda" ]]; then
-    # TODO: This is better to be passed as a parameter from _linux-test workflow
-    # so that it can be consistent with what is set in build
-    TORCH_CUDA_ARCH_LIST="8.0;8.6" pip_install --no-use-pep517 --user "git+https://github.com/pytorch/audio.git@${commit}"
-  else
-    pip_install --no-use-pep517 --user "git+https://github.com/pytorch/audio.git@${commit}"
-  fi
-
-}
-
 function install_torchtext() {
-  local data_commit
-  local text_commit
-  data_commit=$(get_pinned_commit data)
-  text_commit=$(get_pinned_commit text)
-  pip_install --no-use-pep517 --user "git+https://github.com/pytorch/data.git@${data_commit}"
-  pip_install --no-use-pep517 --user "git+https://github.com/pytorch/text.git@${text_commit}"
+  local commit
+  commit=$(get_pinned_commit text)
+  pip_install --no-use-pep517 --user "git+https://github.com/pytorch/text.git@${commit}"
 }
 
 function install_torchvision() {
-  local orig_preload
   local commit
   commit=$(get_pinned_commit vision)
-  orig_preload=${LD_PRELOAD}
-  if [ -n "${LD_PRELOAD}" ]; then
-    # Silence dlerror to work-around glibc ASAN bug, see https://sourceware.org/bugzilla/show_bug.cgi?id=27653#c9
-    echo 'char* dlerror(void) { return "";}'|gcc -fpic -shared -o "${HOME}/dlerror.so" -x c -
-    LD_PRELOAD=${orig_preload}:${HOME}/dlerror.so
-  fi
   pip_install --no-use-pep517 --user "git+https://github.com/pytorch/vision.git@${commit}"
-  if [ -n "${LD_PRELOAD}" ]; then
-    LD_PRELOAD=${orig_preload}
-  fi
-}
-
-function install_tlparse() {
-  pip_install --user "tlparse==0.3.7"
-  PATH="$(python -m site --user-base)/bin:$PATH"
-}
-
-function install_torchrec_and_fbgemm() {
-  local torchrec_commit
-  torchrec_commit=$(get_pinned_commit torchrec)
-  local fbgemm_commit
-  fbgemm_commit=$(get_pinned_commit fbgemm)
-  pip_uninstall torchrec-nightly
-  pip_uninstall fbgemm-gpu-nightly
-  pip_install setuptools-git-versioning scikit-build pyre-extensions
-  # See https://github.com/pytorch/pytorch/issues/106971
-  CUDA_PATH=/usr/local/cuda-12.1 pip_install --no-use-pep517 --user "git+https://github.com/pytorch/FBGEMM.git@${fbgemm_commit}#egg=fbgemm-gpu&subdirectory=fbgemm_gpu"
-  pip_install --no-use-pep517 --user "git+https://github.com/pytorch/torchrec.git@${torchrec_commit}"
 }
 
 function clone_pytorch_xla() {
   if [[ ! -d ./xla ]]; then
-    git clone --recursive --quiet https://github.com/pytorch/xla.git
+    git clone --recursive -b r2.0 --quiet https://github.com/pytorch/xla.git
     pushd xla
     # pin the xla hash so that we don't get broken by changes to xla
     git checkout "$(cat ../.github/ci_commit_pins/xla.txt)"
@@ -188,15 +129,53 @@ function clone_pytorch_xla() {
   fi
 }
 
+function install_filelock() {
+  pip_install filelock
+}
+
+function install_triton() {
+  local commit
+  if [[ "${TEST_CONFIG}" == *rocm* ]]; then
+    echo "skipping triton due to rocm"
+  else
+    commit=$(get_pinned_commit triton)
+    if [[ "${BUILD_ENVIRONMENT}" == *gcc7* ]]; then
+      # Trition needs gcc-9 to build
+      sudo apt-get install -y g++-9
+      CXX=g++-9 pip_install --user "git+https://github.com/openai/triton@${commit}#subdirectory=python"
+    elif [[ "${BUILD_ENVIRONMENT}" == *clang* ]]; then
+      # Trition needs <filesystem> which surprisingly is not available with clang-9 toolchain
+      sudo add-apt-repository -y ppa:ubuntu-toolchain-r/test
+      sudo apt-get install -y g++-9
+      CXX=g++-9 pip_install --user "git+https://github.com/openai/triton@${commit}#subdirectory=python"
+    else
+      pip_install --user "git+https://github.com/openai/triton@${commit}#subdirectory=python"
+    fi
+    pip_install --user jinja2
+  fi
+}
+
+function setup_torchdeploy_deps(){
+  conda install -y -n "py_${ANACONDA_PYTHON_VERSION}" "libpython-static=${ANACONDA_PYTHON_VERSION}"
+  local CC
+  local CXX
+  CC="$(which gcc)"
+  CXX="$(which g++)"
+  export CC
+  export CXX
+  pip install --upgrade pip
+}
+
 function checkout_install_torchdeploy() {
   local commit
   commit=$(get_pinned_commit multipy)
+  setup_torchdeploy_deps
   pushd ..
   git clone --recurse-submodules https://github.com/pytorch/multipy.git
   pushd multipy
   git checkout "${commit}"
   python multipy/runtime/example/generate_examples.py
-  BUILD_CUDA_TESTS=1 pip install -e .
+  pip install -e . --install-option="--cudatests"
   popd
   popd
 }
@@ -210,12 +189,26 @@ function test_torch_deploy(){
  popd
 }
 
-function checkout_install_torchbench() {
+function install_huggingface() {
   local commit
-  commit=$(get_pinned_commit torchbench)
+  commit=$(get_pinned_commit huggingface)
+  pip_install pandas
+  pip_install scipy
+  pip_install "git+https://github.com/huggingface/transformers.git@${commit}#egg=transformers"
+}
+
+function install_timm() {
+  local commit
+  commit=$(get_pinned_commit timm)
+  pip_install pandas
+  pip_install scipy
+  pip_install "git+https://github.com/rwightman/pytorch-image-models@${commit}"
+}
+
+function checkout_install_torchbench() {
   git clone https://github.com/pytorch/benchmark torchbench
   pushd torchbench
-  git checkout "$commit"
+  git checkout no_torchaudio
 
   if [ "$1" ]; then
     python install.py --continue_on_fail models "$@"
@@ -227,6 +220,10 @@ function checkout_install_torchbench() {
   popd
 }
 
+function test_functorch() {
+  python test/run_test.py --functorch --verbose
+}
+
 function print_sccache_stats() {
   echo 'PyTorch Build Statistics'
   sccache --show-stats

.ci/pytorch/create_test_cert.py

@@ -1,10 +1,10 @@
 from datetime import datetime, timedelta
 from tempfile import mkdtemp
-
-from cryptography import x509
-from cryptography.hazmat.primitives import hashes, serialization
+from cryptography.hazmat.primitives import serialization
 from cryptography.hazmat.primitives.asymmetric import rsa
+from cryptography import x509
 from cryptography.x509.oid import NameOID
+from cryptography.hazmat.primitives import hashes
 
 temp_dir = mkdtemp()
 print(temp_dir)
@@ -16,43 +16,37 @@ def genrsa(path):
         key_size=2048,
     )
     with open(path, "wb") as f:
-        f.write(
-            key.private_bytes(
-                encoding=serialization.Encoding.PEM,
-                format=serialization.PrivateFormat.TraditionalOpenSSL,
-                encryption_algorithm=serialization.NoEncryption(),
-            )
-        )
+        f.write(key.private_bytes(
+            encoding=serialization.Encoding.PEM,
+            format=serialization.PrivateFormat.TraditionalOpenSSL,
+            encryption_algorithm=serialization.NoEncryption(),
+        ))
     return key
 
 
 def create_cert(path, C, ST, L, O, key):
-    subject = issuer = x509.Name(
-        [
-            x509.NameAttribute(NameOID.COUNTRY_NAME, C),
-            x509.NameAttribute(NameOID.STATE_OR_PROVINCE_NAME, ST),
-            x509.NameAttribute(NameOID.LOCALITY_NAME, L),
-            x509.NameAttribute(NameOID.ORGANIZATION_NAME, O),
-        ]
-    )
-    cert = (
-        x509.CertificateBuilder()
-        .subject_name(subject)
-        .issuer_name(issuer)
-        .public_key(key.public_key())
-        .serial_number(x509.random_serial_number())
-        .not_valid_before(datetime.utcnow())
-        .not_valid_after(
-            # Our certificate will be valid for 10 days
-            datetime.utcnow()
-            + timedelta(days=10)
-        )
-        .add_extension(
-            x509.BasicConstraints(ca=True, path_length=None),
-            critical=True,
-        )
-        .sign(key, hashes.SHA256())
-    )
+    subject = issuer = x509.Name([
+        x509.NameAttribute(NameOID.COUNTRY_NAME, C),
+        x509.NameAttribute(NameOID.STATE_OR_PROVINCE_NAME, ST),
+        x509.NameAttribute(NameOID.LOCALITY_NAME, L),
+        x509.NameAttribute(NameOID.ORGANIZATION_NAME, O),
+    ])
+    cert = x509.CertificateBuilder().subject_name(
+        subject
+    ).issuer_name(
+        issuer
+    ).public_key(
+        key.public_key()
+    ).serial_number(
+        x509.random_serial_number()
+    ).not_valid_before(
+        datetime.utcnow()
+    ).not_valid_after(
+        # Our certificate will be valid for 10 days
+        datetime.utcnow() + timedelta(days=10)
+    ).add_extension(
+        x509.BasicConstraints(ca=True, path_length=None), critical=True,
+    ).sign(key, hashes.SHA256())
     # Write our certificate out to disk.
     with open(path, "wb") as f:
         f.write(cert.public_bytes(serialization.Encoding.PEM))
@@ -60,65 +54,43 @@ def create_cert(path, C, ST, L, O, key):
 
 
 def create_req(path, C, ST, L, O, key):
-    csr = (
-        x509.CertificateSigningRequestBuilder()
-        .subject_name(
-            x509.Name(
-                [
-                    # Provide various details about who we are.
-                    x509.NameAttribute(NameOID.COUNTRY_NAME, C),
-                    x509.NameAttribute(NameOID.STATE_OR_PROVINCE_NAME, ST),
-                    x509.NameAttribute(NameOID.LOCALITY_NAME, L),
-                    x509.NameAttribute(NameOID.ORGANIZATION_NAME, O),
-                ]
-            )
-        )
-        .sign(key, hashes.SHA256())
-    )
+    csr = x509.CertificateSigningRequestBuilder().subject_name(x509.Name([
+        # Provide various details about who we are.
+        x509.NameAttribute(NameOID.COUNTRY_NAME, C),
+        x509.NameAttribute(NameOID.STATE_OR_PROVINCE_NAME, ST),
+        x509.NameAttribute(NameOID.LOCALITY_NAME, L),
+        x509.NameAttribute(NameOID.ORGANIZATION_NAME, O),
+    ])).sign(key, hashes.SHA256())
     with open(path, "wb") as f:
         f.write(csr.public_bytes(serialization.Encoding.PEM))
     return csr
 
 
 def sign_certificate_request(path, csr_cert, ca_cert, private_ca_key):
-    cert = (
-        x509.CertificateBuilder()
-        .subject_name(csr_cert.subject)
-        .issuer_name(ca_cert.subject)
-        .public_key(csr_cert.public_key())
-        .serial_number(x509.random_serial_number())
-        .not_valid_before(datetime.utcnow())
-        .not_valid_after(
-            # Our certificate will be valid for 10 days
-            datetime.utcnow()
-            + timedelta(days=10)
-            # Sign our certificate with our private key
-        )
-        .sign(private_ca_key, hashes.SHA256())
-    )
+    cert = x509.CertificateBuilder().subject_name(
+        csr_cert.subject
+    ).issuer_name(
+        ca_cert.subject
+    ).public_key(
+        csr_cert.public_key()
+    ).serial_number(
+        x509.random_serial_number()
+    ).not_valid_before(
+        datetime.utcnow()
+    ).not_valid_after(
+        # Our certificate will be valid for 10 days
+        datetime.utcnow() + timedelta(days=10)
+        # Sign our certificate with our private key
+    ).sign(private_ca_key, hashes.SHA256())
     with open(path, "wb") as f:
         f.write(cert.public_bytes(serialization.Encoding.PEM))
     return cert
 
 
 ca_key = genrsa(temp_dir + "/ca.key")
-ca_cert = create_cert(
-    temp_dir + "/ca.pem",
-    "US",
-    "New York",
-    "New York",
-    "Gloo Certificate Authority",
-    ca_key,
-)
+ca_cert = create_cert(temp_dir + "/ca.pem", u"US", u"New York", u"New York", u"Gloo Certificate Authority", ca_key)
 
 pkey = genrsa(temp_dir + "/pkey.key")
-csr = create_req(
-    temp_dir + "/csr.csr",
-    "US",
-    "California",
-    "San Francisco",
-    "Gloo Testing Company",
-    pkey,
-)
+csr = create_req(temp_dir + "/csr.csr", u"US", u"California", u"San Francisco", u"Gloo Testing Company", pkey)
 
 cert = sign_certificate_request(temp_dir + "/cert.pem", csr, ca_cert, ca_key)

.ci/pytorch/docs-test.sh

@@ -6,4 +6,5 @@ source "$(dirname "${BASH_SOURCE[0]}")/common.sh"
 echo "Testing pytorch docs"
 
 cd docs
-TERM=vt100 make doctest
+pip_install -r requirements.txt
+make doctest

.ci/pytorch/functorch_doc_push_script.sh

@@ -1,40 +0,0 @@
-#!/bin/bash
-
-# This is where the local pytorch install in the docker image is located
-pt_checkout="/var/lib/jenkins/workspace"
-source "$pt_checkout/.ci/pytorch/common_utils.sh"
-echo "functorch_doc_push_script.sh: Invoked with $*"
-
-set -ex
-
-version=${DOCS_VERSION:-nightly}
-echo "version: $version"
-
-# Build functorch docs
-pushd $pt_checkout/functorch/docs
-make html
-popd
-
-git clone https://github.com/pytorch/functorch -b gh-pages --depth 1 functorch_ghpages
-pushd functorch_ghpages
-
-if [ "$version" == "main" ]; then
-  version=nightly
-fi
-
-git rm -rf "$version" || true
-mv "$pt_checkout/functorch/docs/build/html" "$version"
-
-git add "$version" || true
-git status
-git config user.email "soumith+bot@pytorch.org"
-git config user.name "pytorchbot"
-# If there aren't changes, don't make a commit; push is no-op
-git commit -m "Generate Python docs from pytorch/pytorch@${GITHUB_SHA}" || true
-git status
-
-if [[ "${WITH_PUSH:-}" == true ]]; then
-  git push -u origin gh-pages
-fi
-
-popd

.ci/pytorch/install_cache_xla.sh

@@ -1,37 +0,0 @@
-#!/bin/bash
-
-# Script for installing sccache on the xla build job, which uses xla's docker
-# image and doesn't have sccache installed on it.  This is mostly copied from
-# .ci/docker/install_cache.sh.  Changes are: removing checks that will always
-# return the same thing, ex checks for for rocm, CUDA, and changing the path
-# where sccache is installed, and not changing /etc/environment.
-
-set -ex
-
-install_binary() {
-  echo "Downloading sccache binary from S3 repo"
-  curl --retry 3 https://s3.amazonaws.com/ossci-linux/sccache -o /tmp/cache/bin/sccache
-}
-
-mkdir -p /tmp/cache/bin
-mkdir -p /tmp/cache/lib
-export PATH="/tmp/cache/bin:$PATH"
-
-install_binary
-chmod a+x /tmp/cache/bin/sccache
-
-function write_sccache_stub() {
-  # Unset LD_PRELOAD for ps because of asan + ps issues
-  # https://gcc.gnu.org/bugzilla/show_bug.cgi?id=90589
-  # shellcheck disable=SC2086
-  # shellcheck disable=SC2059
-  printf "#!/bin/sh\nif [ \$(env -u LD_PRELOAD ps -p \$PPID -o comm=) != sccache ]; then\n  exec sccache $(which $1) \"\$@\"\nelse\n  exec $(which $1) \"\$@\"\nfi" > "/tmp/cache/bin/$1"
-  chmod a+x "/tmp/cache/bin/$1"
-}
-
-write_sccache_stub cc
-write_sccache_stub c++
-write_sccache_stub gcc
-write_sccache_stub g++
-write_sccache_stub clang
-write_sccache_stub clang++

.ci/pytorch/macos-build.sh

@@ -40,14 +40,8 @@ cross_compile_arm64() {
   USE_DISTRIBUTED=0 CMAKE_OSX_ARCHITECTURES=arm64 MACOSX_DEPLOYMENT_TARGET=11.0 USE_MKLDNN=OFF USE_QNNPACK=OFF WERROR=1 BUILD_TEST=OFF USE_PYTORCH_METAL=1 python setup.py bdist_wheel
 }
 
-compile_arm64() {
-  # Compilation for arm64
-  # TODO: Compile with OpenMP support (but this causes CI regressions as cross-compilation were done with OpenMP disabled)
-  USE_DISTRIBUTED=0 USE_OPENMP=1 MACOSX_DEPLOYMENT_TARGET=11.0 WERROR=1 BUILD_TEST=OFF USE_PYTORCH_METAL=1 python setup.py bdist_wheel
-}
-
 compile_x86_64() {
-  USE_DISTRIBUTED=0 WERROR=1 python setup.py bdist_wheel --plat-name=macosx_10_9_x86_64
+  USE_DISTRIBUTED=0 WERROR=1 python setup.py bdist_wheel
 }
 
 build_lite_interpreter() {
@@ -68,14 +62,8 @@ build_lite_interpreter() {
     "${CPP_BUILD}/caffe2/build/bin/test_lite_interpreter_runtime"
 }
 
-print_cmake_info
-
 if [[ ${BUILD_ENVIRONMENT} = *arm64* ]]; then
-  if [[ $(uname -m) == "arm64" ]]; then
-    compile_arm64
-  else
-    cross_compile_arm64
-  fi
+  cross_compile_arm64
 elif [[ ${BUILD_ENVIRONMENT} = *lite-interpreter* ]]; then
   export BUILD_LITE_INTERPRETER=1
   build_lite_interpreter

.ci/pytorch/macos-common.sh

@@ -9,25 +9,6 @@ sysctl -a | grep machdep.cpu
 
 # These are required for both the build job and the test job.
 # In the latter to test cpp extensions.
-export MACOSX_DEPLOYMENT_TARGET=11.1
+export MACOSX_DEPLOYMENT_TARGET=10.9
 export CXX=clang++
 export CC=clang
-
-print_cmake_info() {
-  CMAKE_EXEC=$(which cmake)
-  echo "$CMAKE_EXEC"
-
-  CONDA_INSTALLATION_DIR=$(dirname "$CMAKE_EXEC")
-  # Print all libraries under cmake rpath for debugging
-  ls -la "$CONDA_INSTALLATION_DIR/../lib"
-
-  export CMAKE_EXEC
-  # Explicitly add conda env lib folder to cmake rpath to address the flaky issue
-  # where cmake dependencies couldn't be found. This seems to point to how conda
-  # links $CMAKE_EXEC to its package cache when cloning a new environment
-  install_name_tool -add_rpath @executable_path/../lib "${CMAKE_EXEC}" || true
-  # Adding the rpath will invalidate cmake signature, so signing it again here
-  # to trust the executable. EXC_BAD_ACCESS (SIGKILL (Code Signature Invalid))
-  # with an exit code 137 otherwise
-  codesign -f -s - "${CMAKE_EXEC}" || true
-}

.ci/pytorch/macos-test.sh

@@ -25,7 +25,6 @@ setup_test_python() {
   # using the address associated with the loopback interface.
   export GLOO_SOCKET_IFNAME=lo0
   echo "Ninja version: $(ninja --version)"
-  echo "Python version: $(which python) ($(python --version))"
 
   # Increase default limit on open file handles from 256 to 1024
   ulimit -n 1024
@@ -71,19 +70,37 @@ test_libtorch() {
     VERBOSE=1 DEBUG=1 python "$BUILD_LIBTORCH_PY"
     popd
 
-    MNIST_DIR="${PWD}/test/cpp/api/mnist"
-    python tools/download_mnist.py --quiet -d "${MNIST_DIR}"
+    python tools/download_mnist.py --quiet -d test/cpp/api/mnist
 
     # Unfortunately it seems like the test can't load from miniconda3
     # without these paths being set
     export DYLD_LIBRARY_PATH="$DYLD_LIBRARY_PATH:$PWD/miniconda3/lib"
     export LD_LIBRARY_PATH="$LD_LIBRARY_PATH:$PWD/miniconda3/lib"
-    TORCH_CPP_TEST_MNIST_PATH="${MNIST_DIR}" CPP_TESTS_DIR="${CPP_BUILD}/caffe2/bin" python test/run_test.py --cpp --verbose -i cpp/test_api
+    TORCH_CPP_TEST_MNIST_PATH="test/cpp/api/mnist" "$CPP_BUILD"/caffe2/bin/test_api
 
     assert_git_not_dirty
   fi
 }
 
+print_cmake_info() {
+  CMAKE_EXEC=$(which cmake)
+  echo "$CMAKE_EXEC"
+
+  CONDA_INSTALLATION_DIR=$(dirname "$CMAKE_EXEC")
+  # Print all libraries under cmake rpath for debugging
+  ls -la "$CONDA_INSTALLATION_DIR/../lib"
+
+  export CMAKE_EXEC
+  # Explicitly add conda env lib folder to cmake rpath to address the flaky issue
+  # where cmake dependencies couldn't be found. This seems to point to how conda
+  # links $CMAKE_EXEC to its package cache when cloning a new environment
+  install_name_tool -add_rpath @executable_path/../lib "${CMAKE_EXEC}" || true
+  # Adding the rpath will invalidate cmake signature, so signing it again here
+  # to trust the executable. EXC_BAD_ACCESS (SIGKILL (Code Signature Invalid))
+  # with an exit code 137 otherwise
+  codesign -f -s - "${CMAKE_EXEC}" || true
+}
+
 test_custom_backend() {
   print_cmake_info
 
@@ -149,9 +166,9 @@ test_jit_hooks() {
   assert_git_not_dirty
 }
 
-install_tlparse
-
-if [[ $NUM_TEST_SHARDS -gt 1 ]]; then
+if [[ "${TEST_CONFIG}" == *functorch* ]]; then
+  test_functorch
+elif [[ $NUM_TEST_SHARDS -gt 1 ]]; then
   test_python_shard "${SHARD_NUMBER}"
   if [[ "${SHARD_NUMBER}" == 1 ]]; then
     test_libtorch

.ci/pytorch/multigpu-test.sh

@@ -8,7 +8,6 @@
 source "$(dirname "${BASH_SOURCE[0]}")/common.sh"
 
 echo "Testing pytorch"
-time python test/run_test.py --include test_cuda_multigpu test_cuda_primary_ctx --verbose
 
 # Disabling tests to see if they solve timeout issues; see https://github.com/pytorch/pytorch/issues/70015
 # python tools/download_mnist.py --quiet -d test/cpp/api/mnist
@@ -18,7 +17,6 @@ time python test/run_test.py --verbose -i distributed/test_c10d_gloo
 time python test/run_test.py --verbose -i distributed/test_c10d_nccl
 time python test/run_test.py --verbose -i distributed/test_c10d_spawn_gloo
 time python test/run_test.py --verbose -i distributed/test_c10d_spawn_nccl
-time python test/run_test.py --verbose -i distributed/test_cuda_p2p
 time python test/run_test.py --verbose -i distributed/test_store
 time python test/run_test.py --verbose -i distributed/test_pg_wrapper
 time python test/run_test.py --verbose -i distributed/rpc/cuda/test_tensorpipe_agent
@@ -29,34 +27,23 @@ time python test/run_test.py --verbose -i distributed/checkpoint/test_checkpoint
 time python test/run_test.py --verbose -i distributed/checkpoint/test_file_system_checkpoint
 time python test/run_test.py --verbose -i distributed/_shard/sharding_spec/test_sharding_spec
 time python test/run_test.py --verbose -i distributed/_shard/sharding_plan/test_sharding_plan
+time python test/run_test.py --verbose -i distributed/_shard/sharded_tensor/test_megatron_prototype
 time python test/run_test.py --verbose -i distributed/_shard/sharded_tensor/test_sharded_tensor
 time python test/run_test.py --verbose -i distributed/_shard/sharded_tensor/test_sharded_tensor_reshard
-
-# functional collective tests
-time python test/run_test.py --verbose -i distributed/test_functional_api
-
-# DTensor tests
-time python test/run_test.py --verbose -i distributed/_tensor/test_random_ops
-time python test/run_test.py --verbose -i distributed/_tensor/test_dtensor_compile
-
-# DeviceMesh test
-time python test/run_test.py --verbose -i distributed/test_device_mesh
-
-# DTensor/TP tests
-time python test/run_test.py --verbose -i distributed/tensor/parallel/test_ddp_2d_parallel
-time python test/run_test.py --verbose -i distributed/tensor/parallel/test_fsdp_2d_parallel
-time python test/run_test.py --verbose -i distributed/tensor/parallel/test_tp_examples
-time python test/run_test.py --verbose -i distributed/tensor/parallel/test_tp_random_state
-
-# FSDP2 tests
-time python test/run_test.py --verbose -i distributed/_composable/fsdp/test_fully_shard_training -- -k test_2d_mlp_with_nd_mesh
-
-# Pipelining composability tests
-time python test/run_test.py --verbose -i distributed/pipelining/test_composability.py
-
+time python test/run_test.py --verbose -i distributed/_shard/sharded_tensor/ops/test_chunk
+time python test/run_test.py --verbose -i distributed/_shard/sharded_tensor/ops/test_elementwise_ops
+time python test/run_test.py --verbose -i distributed/_shard/sharded_tensor/ops/test_embedding
+time python test/run_test.py --verbose -i distributed/_shard/sharded_tensor/ops/test_embedding_bag
+time python test/run_test.py --verbose -i distributed/_shard/sharded_tensor/ops/test_binary_cmp
+time python test/run_test.py --verbose -i distributed/_shard/sharded_tensor/ops/test_init
+time python test/run_test.py --verbose -i distributed/_shard/sharded_tensor/ops/test_linear
+time python test/run_test.py --verbose -i distributed/_shard/sharded_tensor/ops/test_math_ops
+time python test/run_test.py --verbose -i distributed/_shard/sharded_tensor/ops/test_matrix_ops
+time python test/run_test.py --verbose -i distributed/_shard/sharded_tensor/ops/test_softmax
+time python test/run_test.py --verbose -i distributed/_shard/sharded_optim/test_sharded_optim
+time python test/run_test.py --verbose -i distributed/_shard/test_partial_tensor
+time python test/run_test.py --verbose -i distributed/_shard/test_replicated_tensor
 # Other tests
 time python test/run_test.py --verbose -i test_cuda_primary_ctx
-time python test/run_test.py --verbose -i test_optim -- -k test_forloop_goes_right_direction_multigpu
-time python test/run_test.py --verbose -i test_optim -- -k test_mixed_device_dtype
-time python test/run_test.py --verbose -i test_foreach -- -k test_tensors_grouping
+time python test/run_test.py --verbose -i test_optim -- -k optimizers_with_varying_tensors
 assert_git_not_dirty

.ci/pytorch/perf_test/compare_with_baseline.py

@@ -1,41 +1,32 @@
-import argparse
+import sys
 import json
 import math
-import sys
+import argparse
 
 parser = argparse.ArgumentParser()
-parser.add_argument(
-    "--test-name", dest="test_name", action="store", required=True, help="test name"
-)
-parser.add_argument(
-    "--sample-stats",
-    dest="sample_stats",
-    action="store",
-    required=True,
-    help="stats from sample",
-)
-parser.add_argument(
-    "--update",
-    action="store_true",
-    help="whether to update baseline using stats from sample",
-)
+parser.add_argument('--test-name', dest='test_name', action='store',
+                    required=True, help='test name')
+parser.add_argument('--sample-stats', dest='sample_stats', action='store',
+                    required=True, help='stats from sample')
+parser.add_argument('--update', action='store_true',
+                    help='whether to update baseline using stats from sample')
 args = parser.parse_args()
 
 test_name = args.test_name
 
-if "cpu" in test_name:
-    backend = "cpu"
-elif "gpu" in test_name:
-    backend = "gpu"
+if 'cpu' in test_name:
+    backend = 'cpu'
+elif 'gpu' in test_name:
+    backend = 'gpu'
 
-data_file_path = f"../{backend}_runtime.json"
+data_file_path = '../{}_runtime.json'.format(backend)
 
 with open(data_file_path) as data_file:
     data = json.load(data_file)
 
 if test_name in data:
-    mean = float(data[test_name]["mean"])
-    sigma = float(data[test_name]["sigma"])
+    mean = float(data[test_name]['mean'])
+    sigma = float(data[test_name]['sigma'])
 else:
     # Let the test pass if baseline number doesn't exist
     mean = sys.maxsize
@@ -52,39 +43,37 @@ if math.isnan(mean) or math.isnan(sigma):
 
 sample_stats_data = json.loads(args.sample_stats)
 
-sample_mean = float(sample_stats_data["mean"])
-sample_sigma = float(sample_stats_data["sigma"])
+sample_mean = float(sample_stats_data['mean'])
+sample_sigma = float(sample_stats_data['sigma'])
 
 print("sample mean: ", sample_mean)
 print("sample sigma: ", sample_sigma)
 
 if math.isnan(sample_mean):
-    raise Exception("""Error: sample mean is NaN""")  # noqa: TRY002
+    raise Exception('''Error: sample mean is NaN''')
 elif math.isnan(sample_sigma):
-    raise Exception("""Error: sample sigma is NaN""")  # noqa: TRY002
+    raise Exception('''Error: sample sigma is NaN''')
 
 z_value = (sample_mean - mean) / sigma
 
 print("z-value: ", z_value)
 
 if z_value >= 3:
-    raise Exception(  # noqa: TRY002
-        f"""\n
+    raise Exception('''\n
 z-value >= 3, there is high chance of perf regression.\n
 To reproduce this regression, run
-`cd .ci/pytorch/perf_test/ && bash {test_name}.sh` on your local machine
+`cd .ci/pytorch/perf_test/ && bash {}.sh` on your local machine
 and compare the runtime before/after your code change.
-"""
-    )
+'''.format(test_name))
 else:
     print("z-value < 3, no perf regression detected.")
     if args.update:
         print("We will use these numbers as new baseline.")
-        new_data_file_path = f"../new_{backend}_runtime.json"
+        new_data_file_path = '../new_{}_runtime.json'.format(backend)
         with open(new_data_file_path) as new_data_file:
             new_data = json.load(new_data_file)
         new_data[test_name] = {}
-        new_data[test_name]["mean"] = sample_mean
-        new_data[test_name]["sigma"] = max(sample_sigma, sample_mean * 0.1)
-        with open(new_data_file_path, "w") as new_data_file:
+        new_data[test_name]['mean'] = sample_mean
+        new_data[test_name]['sigma'] = max(sample_sigma, sample_mean * 0.1)
+        with open(new_data_file_path, 'w') as new_data_file:
             json.dump(new_data, new_data_file, indent=4)

.ci/pytorch/perf_test/get_stats.py

@@ -1,6 +1,5 @@
-import json
 import sys
-
+import json
 import numpy
 
 sample_data_list = sys.argv[1:]
@@ -10,8 +9,8 @@ sample_mean = numpy.mean(sample_data_list)
 sample_sigma = numpy.std(sample_data_list)
 
 data = {
-    "mean": sample_mean,
-    "sigma": sample_sigma,
+    'mean': sample_mean,
+    'sigma': sample_sigma,
 }
 
 print(json.dumps(data))

.ci/pytorch/perf_test/update_commit_hash.py

@@ -1,5 +1,5 @@
-import json
 import sys
+import json
 
 data_file_path = sys.argv[1]
 commit_hash = sys.argv[2]
@@ -7,7 +7,7 @@ commit_hash = sys.argv[2]
 with open(data_file_path) as data_file:
     data = json.load(data_file)
 
-data["commit"] = commit_hash
+data['commit'] = commit_hash
 
-with open(data_file_path, "w") as data_file:
+with open(data_file_path, 'w') as data_file:
     json.dump(data, data_file)

.ci/pytorch/print_sccache_log.py

@@ -9,9 +9,9 @@ for line in lines:
     # Ignore errors from CPU instruction set, symbol existing testing,
     # or compilation error formatting
     ignored_keywords = [
-        "src.c",
-        "CheckSymbolExists.c",
-        "test_compilation_error_formatting",
+        'src.c',
+        'CheckSymbolExists.c',
+        'test_compilation_error_formatting',
     ]
-    if all(keyword not in line for keyword in ignored_keywords):
+    if all([keyword not in line for keyword in ignored_keywords]):
         print(line)

.ci/pytorch/win-build.sh

@@ -15,6 +15,13 @@ source "$SCRIPT_PARENT_DIR/common.sh"
 # shellcheck source=./common-build.sh
 source "$SCRIPT_PARENT_DIR/common-build.sh"
 
+IMAGE_COMMIT_ID=$(git rev-parse HEAD)
+export IMAGE_COMMIT_ID
+export IMAGE_COMMIT_TAG=${BUILD_ENVIRONMENT}-${IMAGE_COMMIT_ID}
+if [[ ${JOB_NAME} == *"develop"* ]]; then
+  export IMAGE_COMMIT_TAG=develop-${IMAGE_COMMIT_TAG}
+fi
+
 export TMP_DIR="${PWD}/build/win_tmp"
 TMP_DIR_WIN=$(cygpath -w "${TMP_DIR}")
 export TMP_DIR_WIN
@@ -23,6 +30,14 @@ if [[ -n "$PYTORCH_FINAL_PACKAGE_DIR" ]]; then
     mkdir -p "$PYTORCH_FINAL_PACKAGE_DIR" || true
 fi
 
+# This directory is used only to hold "pytorch_env_restore.bat", called via "setup_pytorch_env.bat"
+CI_SCRIPTS_DIR=$TMP_DIR/ci_scripts
+mkdir -p "$CI_SCRIPTS_DIR"
+
+if [ -n "$(ls "$CI_SCRIPTS_DIR"/*)" ]; then
+    rm "$CI_SCRIPTS_DIR"/*
+fi
+
 export SCRIPT_HELPERS_DIR=$SCRIPT_PARENT_DIR/win-test-helpers
 
 set +ex
@@ -44,4 +59,7 @@ set -ex
 
 assert_git_not_dirty
 
+if [ ! -f "${TMP_DIR}"/"${IMAGE_COMMIT_TAG}".7z ] && [ ! "${BUILD_ENVIRONMENT}" == "" ]; then
+    exit 1
+fi
 echo "BUILD PASSED"

.ci/pytorch/win-test-helpers/build_pytorch.bat

@@ -16,23 +16,24 @@ set PATH=C:\Program Files\CMake\bin;C:\Program Files\7-Zip;C:\ProgramData\chocol
 
 set INSTALLER_DIR=%SCRIPT_HELPERS_DIR%\installation-helpers
 
+
+call %INSTALLER_DIR%\install_mkl.bat
+if errorlevel 1 exit /b
+if not errorlevel 0 exit /b
+
 call %INSTALLER_DIR%\install_magma.bat
-if errorlevel 1 goto fail
-if not errorlevel 0 goto fail
+if errorlevel 1 exit /b
+if not errorlevel 0 exit /b
 
 call %INSTALLER_DIR%\install_sccache.bat
-if errorlevel 1 goto fail
-if not errorlevel 0 goto fail
+if errorlevel 1 exit /b
+if not errorlevel 0 exit /b
 
 :: Miniconda has been installed as part of the Windows AMI with all the dependencies.
 :: We just need to activate it here
 call %INSTALLER_DIR%\activate_miniconda3.bat
-if errorlevel 1 goto fail
-if not errorlevel 0 goto fail
-
-call pip install mkl-include==2021.4.0 mkl-devel==2021.4.0
-if errorlevel 1 goto fail
-if not errorlevel 0 goto fail
+if errorlevel 1 exit /b
+if not errorlevel 0 exit /b
 
 :: Override VS env here
 pushd .
@@ -41,8 +42,8 @@ if "%VC_VERSION%" == "" (
 ) else (
     call "C:\Program Files (x86)\Microsoft Visual Studio\%VC_YEAR%\%VC_PRODUCT%\VC\Auxiliary\Build\vcvarsall.bat" x64 -vcvars_ver=%VC_VERSION%
 )
-if errorlevel 1 goto fail
-if not errorlevel 0 goto fail
+if errorlevel 1 exit /b
+if not errorlevel 0 exit /b
 @echo on
 popd
 
@@ -52,12 +53,12 @@ set CUDA_PATH=C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v%CUDA_VERSION%
 
 if x%CUDA_VERSION:.=%==x%CUDA_VERSION% (
     echo CUDA version %CUDA_VERSION% format isn't correct, which doesn't contain '.'
-    goto fail
+    exit /b 1
 )
 rem version transformer, for example 10.1 to 10_1.
 if x%CUDA_VERSION:.=%==x%CUDA_VERSION% (
     echo CUDA version %CUDA_VERSION% format isn't correct, which doesn't contain '.'
-    goto fail
+    exit /b 1
 )
 set VERSION_SUFFIX=%CUDA_VERSION:.=_%
 set CUDA_PATH_V%VERSION_SUFFIX%=%CUDA_PATH%
@@ -88,8 +89,8 @@ set SCCACHE_IGNORE_SERVER_IO_ERROR=1
 sccache --stop-server
 sccache --start-server
 sccache --zero-stats
-set CMAKE_C_COMPILER_LAUNCHER=sccache
-set CMAKE_CXX_COMPILER_LAUNCHER=sccache
+set CC=sccache-cl
+set CXX=sccache-cl
 
 set CMAKE_GENERATOR=Ninja
 
@@ -101,8 +102,8 @@ if "%USE_CUDA%"=="1" (
   :: CMake requires a single command as CUDA_NVCC_EXECUTABLE, so we push the wrappers
   :: randomtemp.exe and sccache.exe into a batch file which CMake invokes.
   curl -kL https://github.com/peterjc123/randomtemp-rust/releases/download/v0.4/randomtemp.exe --output %TMP_DIR_WIN%\bin\randomtemp.exe
-  if errorlevel 1 goto fail
-  if not errorlevel 0 goto fail
+  if errorlevel 1 exit /b
+  if not errorlevel 0 exit /b
   echo @"%TMP_DIR_WIN%\bin\randomtemp.exe" "%TMP_DIR_WIN%\bin\sccache.exe" "%CUDA_PATH%\bin\nvcc.exe" %%* > "%TMP_DIR%/bin/nvcc.bat"
   cat %TMP_DIR%/bin/nvcc.bat
   set CUDA_NVCC_EXECUTABLE=%TMP_DIR%/bin/nvcc.bat
@@ -110,23 +111,45 @@ if "%USE_CUDA%"=="1" (
   set CMAKE_CUDA_COMPILER_LAUNCHER=%TMP_DIR%/bin/randomtemp.exe;%TMP_DIR%\bin\sccache.exe
 )
 
-:: Print all existing environment variable for debugging
-set
+@echo off
+echo @echo off >> %TMP_DIR_WIN%\ci_scripts\pytorch_env_restore.bat
+for /f "usebackq tokens=*" %%i in (`set`) do echo set "%%i" >> %TMP_DIR_WIN%\ci_scripts\pytorch_env_restore.bat
+@echo on
+
+if "%REBUILD%" == "" (
+  if NOT "%BUILD_ENVIRONMENT%" == "" (
+    :: Create a shortcut to restore pytorch environment
+    echo @echo off >> %TMP_DIR_WIN%/ci_scripts/pytorch_env_restore_helper.bat
+    echo call "%TMP_DIR_WIN%/ci_scripts/pytorch_env_restore.bat" >> %TMP_DIR_WIN%/ci_scripts/pytorch_env_restore_helper.bat
+    echo cd /D "%CD%" >> %TMP_DIR_WIN%/ci_scripts/pytorch_env_restore_helper.bat
+
+    aws s3 cp "s3://ossci-windows/Restore PyTorch Environment.lnk" "C:\Users\circleci\Desktop\Restore PyTorch Environment.lnk"
+    if errorlevel 1 exit /b
+    if not errorlevel 0 exit /b
+  )
+)
 
 python setup.py bdist_wheel
-if errorlevel 1 goto fail
-if not errorlevel 0 goto fail
+if errorlevel 1 exit /b
+if not errorlevel 0 exit /b
 sccache --show-stats
 python -c "import os, glob; os.system('python -mpip install --no-index --no-deps ' + glob.glob('dist/*.whl')[0])"
 (
   if "%BUILD_ENVIRONMENT%"=="" (
     echo NOTE: To run `import torch`, please make sure to activate the conda environment by running `call %CONDA_PARENT_DIR%\Miniconda3\Scripts\activate.bat %CONDA_PARENT_DIR%\Miniconda3` in Command Prompt before running Git Bash.
   ) else (
-    copy /Y "dist\*.whl" "%PYTORCH_FINAL_PACKAGE_DIR%"
+    if "%USE_CUDA%"=="1" (
+        7z a %TMP_DIR_WIN%\%IMAGE_COMMIT_TAG%.7z %CONDA_PARENT_DIR%\Miniconda3\Lib\site-packages\torch %CONDA_PARENT_DIR%\Miniconda3\Lib\site-packages\torchgen %CONDA_PARENT_DIR%\Miniconda3\Lib\site-packages\functorch %CONDA_PARENT_DIR%\Miniconda3\Lib\site-packages\nvfuser && copy /Y "%TMP_DIR_WIN%\%IMAGE_COMMIT_TAG%.7z" "%PYTORCH_FINAL_PACKAGE_DIR%\"
+    ) else (
+        7z a %TMP_DIR_WIN%\%IMAGE_COMMIT_TAG%.7z %CONDA_PARENT_DIR%\Miniconda3\Lib\site-packages\torch %CONDA_PARENT_DIR%\Miniconda3\Lib\site-packages\torchgen %CONDA_PARENT_DIR%\Miniconda3\Lib\site-packages\functorch && copy /Y "%TMP_DIR_WIN%\%IMAGE_COMMIT_TAG%.7z" "%PYTORCH_FINAL_PACKAGE_DIR%\"
+    )
+
+    if errorlevel 1 exit /b
+    if not errorlevel 0 exit /b
 
     :: export test times so that potential sharded tests that'll branch off this build will use consistent data
     python tools/stats/export_test_times.py
-    robocopy /E ".additional_ci_files" "%PYTORCH_FINAL_PACKAGE_DIR%\.additional_ci_files"
+    copy /Y ".pytorch-test-times.json" "%PYTORCH_FINAL_PACKAGE_DIR%"
 
     :: Also save build/.ninja_log as an artifact
     copy /Y "build\.ninja_log" "%PYTORCH_FINAL_PACKAGE_DIR%\"
@@ -135,8 +158,3 @@ python -c "import os, glob; os.system('python -mpip install --no-index --no-deps
 
 sccache --show-stats --stats-format json | jq .stats > sccache-stats-%BUILD_ENVIRONMENT%-%OUR_GITHUB_JOB_ID%.json
 sccache --stop-server
-
-exit /b 0
-
-:fail
-exit /b 1

.ci/pytorch/win-test-helpers/install_test_functorch.bat

@@ -0,0 +1,19 @@
+call %SCRIPT_HELPERS_DIR%\setup_pytorch_env.bat
+:: exit the batch once there's an error
+if not errorlevel 0 (
+  echo "setup pytorch env failed"
+  echo %errorlevel%
+  exit /b
+)
+
+echo "Test functorch"
+pushd test
+python run_test.py --functorch --shard "%SHARD_NUMBER%" "%NUM_TEST_SHARDS%" --verbose
+popd
+if ERRORLEVEL 1 goto fail
+
+:eof
+exit /b 0
+
+:fail
+exit /b 1

.ci/pytorch/win-test-helpers/installation-helpers/install_mkl.bat

@@ -0,0 +1,14 @@
+if "%REBUILD%"=="" (
+  if "%BUILD_ENVIRONMENT%"=="" (
+    curl --retry 3 --retry-all-errors -k https://s3.amazonaws.com/ossci-windows/mkl_2020.2.254.7z --output %TMP_DIR_WIN%\mkl.7z
+  ) else (
+    aws s3 cp s3://ossci-windows/mkl_2020.2.254.7z %TMP_DIR_WIN%\mkl.7z --quiet
+  )
+  if errorlevel 1 exit /b
+  if not errorlevel 0 exit /b
+  7z x -aoa %TMP_DIR_WIN%\mkl.7z -o%TMP_DIR_WIN%\mkl
+  if errorlevel 1 exit /b
+  if not errorlevel 0 exit /b
+)
+set CMAKE_INCLUDE_PATH=%TMP_DIR_WIN%\mkl\include
+set LIB=%TMP_DIR_WIN%\mkl\lib;%LIB%

.ci/pytorch/win-test-helpers/installation-helpers/install_sccache.bat

@@ -1,13 +1,18 @@
 mkdir %TMP_DIR_WIN%\bin
 
 if "%REBUILD%"=="" (
-  IF EXIST %TMP_DIR_WIN%\bin\sccache.exe (
+  :check_sccache
+  %TMP_DIR_WIN%\bin\sccache.exe --show-stats || (
     taskkill /im sccache.exe /f /t || ver > nul
     del %TMP_DIR_WIN%\bin\sccache.exe || ver > nul
+    del %TMP_DIR_WIN%\bin\sccache-cl.exe || ver > nul
+    if "%BUILD_ENVIRONMENT%"=="" (
+      curl --retry 3 --retry-all-errors -k https://s3.amazonaws.com/ossci-windows/sccache.exe --output %TMP_DIR_WIN%\bin\sccache.exe
+      curl --retry 3 --retry-all-errors -k https://s3.amazonaws.com/ossci-windows/sccache-cl.exe --output %TMP_DIR_WIN%\bin\sccache-cl.exe
+    ) else (
+      aws s3 cp s3://ossci-windows/sccache.exe %TMP_DIR_WIN%\bin\sccache.exe
+      aws s3 cp s3://ossci-windows/sccache-cl.exe %TMP_DIR_WIN%\bin\sccache-cl.exe
+    )
+    goto :check_sccache
   )
-  if "%BUILD_ENVIRONMENT%"=="" (
-    curl --retry 3 --retry-all-errors -k https://s3.amazonaws.com/ossci-windows/sccache-v0.7.4.exe --output %TMP_DIR_WIN%\bin\sccache.exe
-  ) else (
-    aws s3 cp s3://ossci-windows/sccache-v0.7.4.exe %TMP_DIR_WIN%\bin\sccache.exe
-  )
-)
+)

.ci/pytorch/win-test-helpers/run_python_nn_smoketests.py

@@ -1,8 +1,7 @@
 #!/usr/bin/env python3
 
-import os
 import subprocess
-import sys
+import os
 
 COMMON_TESTS = [
     (
@@ -32,7 +31,8 @@ GPU_TESTS = [
 
 
 if __name__ == "__main__":
-    if "USE_CUDA" in os.environ and os.environ["USE_CUDA"] == "1":
+
+    if 'USE_CUDA' in os.environ and os.environ['USE_CUDA'] == '1':
         TESTS = COMMON_TESTS + GPU_TESTS
     else:
         TESTS = COMMON_TESTS
@@ -44,14 +44,12 @@ if __name__ == "__main__":
         try:
             subprocess.check_call(command_args)
         except subprocess.CalledProcessError as e:
-            sdk_root = os.environ.get(
-                "WindowsSdkDir", "C:\\Program Files (x86)\\Windows Kits\\10"
-            )
-            debugger = os.path.join(sdk_root, "Debuggers", "x64", "cdb.exe")
+            sdk_root = os.environ.get('WindowsSdkDir', 'C:\\Program Files (x86)\\Windows Kits\\10')
+            debugger = os.path.join(sdk_root, 'Debuggers', 'x64', 'cdb.exe')
             if os.path.exists(debugger):
                 command_args = [debugger, "-o", "-c", "~*g; q"] + command_args
                 command_string = " ".join(command_args)
                 print("Reruning with traceback enabled")
                 print("Command:", command_string)
                 subprocess.run(command_args, check=False)
-            sys.exit(e.returncode)
+            exit(e.returncode)

.ci/pytorch/win-test-helpers/setup_pytorch_env.bat

@@ -1,3 +1,8 @@
+if exist "%TMP_DIR%/ci_scripts/pytorch_env_restore.bat" (
+    call %TMP_DIR%/ci_scripts/pytorch_env_restore.bat
+    exit /b 0
+)
+
 set PATH=C:\Program Files\CMake\bin;C:\Program Files\7-Zip;C:\ProgramData\chocolatey\bin;C:\Program Files\Git\cmd;C:\Program Files\Amazon\AWSCLI;C:\Program Files\Amazon\AWSCLI\bin;%PATH%
 
 :: Install Miniconda3
@@ -9,13 +14,6 @@ call %INSTALLER_DIR%\activate_miniconda3.bat
 if errorlevel 1 exit /b
 if not errorlevel 0 exit /b
 
-:: PyTorch is now installed using the standard wheel on Windows into the conda environment.
-:: However, the test scripts are still frequently referring to the workspace temp directory
-:: build\torch. Rather than changing all these references, making a copy of torch folder
-:: from conda to the current workspace is easier. The workspace will be cleaned up after
-:: the job anyway
-xcopy /s %CONDA_PARENT_DIR%\Miniconda3\Lib\site-packages\torch %TMP_DIR_WIN%\build\torch\
-
 pushd .
 if "%VC_VERSION%" == "" (
     call "C:\Program Files (x86)\Microsoft Visual Studio\%VC_YEAR%\%VC_PRODUCT%\VC\Auxiliary\Build\vcvarsall.bat" x64
@@ -50,5 +48,26 @@ set NUMBAPRO_NVVM=%CUDA_PATH%\nvvm\bin\nvvm64_32_0.dll
 
 set PYTHONPATH=%TMP_DIR_WIN%\build;%PYTHONPATH%
 
-:: Print all existing environment variable for debugging
-set
+if NOT "%BUILD_ENVIRONMENT%"=="" (
+    pushd %TMP_DIR_WIN%\build
+    copy /Y %PYTORCH_FINAL_PACKAGE_DIR_WIN%\%IMAGE_COMMIT_TAG%.7z %TMP_DIR_WIN%\
+    :: 7z: -aos skips if exists because this .bat can be called multiple times
+    7z x %TMP_DIR_WIN%\%IMAGE_COMMIT_TAG%.7z -aos
+    popd
+) else (
+    xcopy /s %CONDA_PARENT_DIR%\Miniconda3\Lib\site-packages\torch %TMP_DIR_WIN%\build\torch\
+)
+
+@echo off
+echo @echo off >> %TMP_DIR_WIN%/ci_scripts/pytorch_env_restore.bat
+for /f "usebackq tokens=*" %%i in (`set`) do echo set "%%i" >> %TMP_DIR_WIN%/ci_scripts/pytorch_env_restore.bat
+@echo on
+
+if NOT "%BUILD_ENVIRONMENT%" == "" (
+  :: Create a shortcut to restore pytorch environment
+  echo @echo off >> %TMP_DIR_WIN%/ci_scripts/pytorch_env_restore_helper.bat
+  echo call "%TMP_DIR_WIN%/ci_scripts/pytorch_env_restore.bat" >> %TMP_DIR_WIN%/ci_scripts/pytorch_env_restore_helper.bat
+  echo cd /D "%CD%" >> %TMP_DIR_WIN%/ci_scripts/pytorch_env_restore_helper.bat
+
+  aws s3 cp "s3://ossci-windows/Restore PyTorch Environment.lnk" "C:\Users\circleci\Desktop\Restore PyTorch Environment.lnk"
+)

.ci/pytorch/win-test-helpers/test_custom_script_ops.bat

@@ -26,6 +26,11 @@ popd
 python test_custom_ops.py -v
 if ERRORLEVEL 1 exit /b 1
 
+:: TODO: fix and re-enable this test
+:: See https://github.com/pytorch/pytorch/issues/25155
+:: python test_custom_classes.py -v
+:: if ERRORLEVEL 1 exit /b 1
+
 python model.py --export-script-module="build/model.pt"
 if ERRORLEVEL 1 exit /b 1
 

.ci/pytorch/win-test-helpers/test_libtorch.bat

@@ -1,54 +1,60 @@
+:: Skip LibTorch tests when building a GPU binary and testing on a CPU machine
+:: because LibTorch tests are not well designed for this use case.
+if "%USE_CUDA%" == "0" IF NOT "%CUDA_VERSION%" == "cpu" exit /b 0
+
 call %SCRIPT_HELPERS_DIR%\setup_pytorch_env.bat
 if errorlevel 1 exit /b 1
 
-:: Save the current working directory so that we can go back there
-set CWD=%cd%
-
-set CPP_TESTS_DIR=%TMP_DIR_WIN%\build\torch\bin
+cd %TMP_DIR_WIN%\build\torch\bin
+set TEST_OUT_DIR=%~dp0\..\..\..\test\test-reports\cpp-unittest
+md %TEST_OUT_DIR%
 set PATH=C:\Program Files\NVIDIA Corporation\NvToolsExt\bin\x64;%TMP_DIR_WIN%\build\torch\lib;%PATH%
 
-set TORCH_CPP_TEST_MNIST_PATH=%CWD%\test\cpp\api\mnist
-python tools\download_mnist.py --quiet -d %TORCH_CPP_TEST_MNIST_PATH%
-
-python test\run_test.py --cpp --verbose -i cpp/test_api
+set TEST_API_OUT_DIR=%TEST_OUT_DIR%\test_api
+md %TEST_API_OUT_DIR%
+test_api.exe --gtest_filter="-IntegrationTest.MNIST*" --gtest_output=xml:%TEST_API_OUT_DIR%\test_api.xml
 if errorlevel 1 exit /b 1
 if not errorlevel 0 exit /b 1
 
 cd %TMP_DIR_WIN%\build\torch\test
 for /r "." %%a in (*.exe) do (
     call :libtorch_check "%%~na" "%%~fa"
-    if errorlevel 1 goto fail
+    if errorlevel 1 exit /b 1
 )
 
 goto :eof
 
 :libtorch_check
-
-cd %CWD%
-set CPP_TESTS_DIR=%TMP_DIR_WIN%\build\torch\test
-
 :: Skip verify_api_visibility as it a compile level test
 if "%~1" == "verify_api_visibility" goto :eof
 
+:: See https://github.com/pytorch/pytorch/issues/25161
+if "%~1" == "c10_metaprogramming_test" goto :eof
+if "%~1" == "module_test" goto :eof
+:: See https://github.com/pytorch/pytorch/issues/25312
+if "%~1" == "converter_nomigraph_test" goto :eof
+:: See https://github.com/pytorch/pytorch/issues/35636
+if "%~1" == "generate_proposals_op_gpu_test" goto :eof
+:: See https://github.com/pytorch/pytorch/issues/35648
+if "%~1" == "reshape_op_gpu_test" goto :eof
+:: See https://github.com/pytorch/pytorch/issues/35651
+if "%~1" == "utility_ops_gpu_test" goto :eof
+
 echo Running "%~2"
 if "%~1" == "c10_intrusive_ptr_benchmark" (
-  :: NB: This is not a gtest executable file, thus couldn't be handled by pytest-cpp
   call "%~2"
   goto :eof
 )
-
-python test\run_test.py --cpp --verbose -i "cpp/%~1"
+:: Differentiating the test report directories is crucial for test time reporting.
+md %TEST_OUT_DIR%\%~n2
+call "%~2" --gtest_output=xml:%TEST_OUT_DIR%\%~n2\%~1.xml
 if errorlevel 1 (
   echo %1 failed with exit code %errorlevel%
-  goto fail
+  exit /b 1
 )
 if not errorlevel 0 (
   echo %1 failed with exit code %errorlevel%
-  goto fail
+  exit /b 1
 )
 
-:eof
-exit /b 0
-
-:fail
-exit /b 1
+goto :eof

.ci/pytorch/win-test-helpers/test_python_jit_legacy.bat

@@ -1,7 +1,7 @@
 call %SCRIPT_HELPERS_DIR%\setup_pytorch_env.bat
 
 echo Copying over test times file
-robocopy /E "%PYTORCH_FINAL_PACKAGE_DIR_WIN%\.additional_ci_files" "%PROJECT_DIR_WIN%\.additional_ci_files"
+copy /Y "%PYTORCH_FINAL_PACKAGE_DIR_WIN%\.pytorch-test-times.json" "%PROJECT_DIR_WIN%"
 
 pushd test
 

.ci/pytorch/win-test-helpers/test_python_shard.bat

@@ -22,7 +22,7 @@ if "%SHARD_NUMBER%" == "1" (
 )
 
 echo Copying over test times file
-robocopy /E "%PYTORCH_FINAL_PACKAGE_DIR_WIN%\.additional_ci_files" "%PROJECT_DIR_WIN%\.additional_ci_files"
+copy /Y "%PYTORCH_FINAL_PACKAGE_DIR_WIN%\.pytorch-test-times.json" "%PROJECT_DIR_WIN%"
 
 echo Run nn tests
 python run_test.py --exclude-jit-executor --exclude-distributed-tests --shard "%SHARD_NUMBER%" "%NUM_TEST_SHARDS%" --verbose

.ci/pytorch/win-test.sh

@@ -5,6 +5,13 @@ SCRIPT_PARENT_DIR=$( cd "$( dirname "${BASH_SOURCE[0]}" )" && pwd )
 # shellcheck source=./common.sh
 source "$SCRIPT_PARENT_DIR/common.sh"
 
+IMAGE_COMMIT_ID=$(git rev-parse HEAD)
+export IMAGE_COMMIT_ID
+export IMAGE_COMMIT_TAG=${BUILD_ENVIRONMENT}-${IMAGE_COMMIT_ID}
+if [[ ${JOB_NAME} == *"develop"* ]]; then
+  export IMAGE_COMMIT_TAG=develop-${IMAGE_COMMIT_TAG}
+fi
+
 export TMP_DIR="${PWD}/build/win_tmp"
 TMP_DIR_WIN=$(cygpath -w "${TMP_DIR}")
 export TMP_DIR_WIN
@@ -14,12 +21,22 @@ export PROJECT_DIR_WIN
 export TEST_DIR="${PWD}/test"
 TEST_DIR_WIN=$(cygpath -w "${TEST_DIR}")
 export TEST_DIR_WIN
-export PYTORCH_FINAL_PACKAGE_DIR="${PYTORCH_FINAL_PACKAGE_DIR:-/c/w/build-results}"
+export PYTORCH_FINAL_PACKAGE_DIR="${PYTORCH_FINAL_PACKAGE_DIR:-/c/users/circleci/workspace/build-results}"
 PYTORCH_FINAL_PACKAGE_DIR_WIN=$(cygpath -w "${PYTORCH_FINAL_PACKAGE_DIR}")
 export PYTORCH_FINAL_PACKAGE_DIR_WIN
 
 mkdir -p "$TMP_DIR"/build/torch
 
+
+# This directory is used only to hold "pytorch_env_restore.bat", called via "setup_pytorch_env.bat"
+CI_SCRIPTS_DIR=$TMP_DIR/ci_scripts
+mkdir -p "$CI_SCRIPTS_DIR"
+
+if [ -n "$(ls "$CI_SCRIPTS_DIR"/*)" ]; then
+    rm "$CI_SCRIPTS_DIR"/*
+fi
+
+
 export SCRIPT_HELPERS_DIR=$SCRIPT_PARENT_DIR/win-test-helpers
 
 if [[ "$TEST_CONFIG" = "force_on_cpu" ]]; then
@@ -34,12 +51,6 @@ if [[ "$BUILD_ENVIRONMENT" == *cuda* ]]; then
   export PYTORCH_TESTING_DEVICE_ONLY_FOR="cuda"
 fi
 
-# TODO: Move both of them to Windows AMI
-python -m pip install pytest-rerunfailures==10.3 pytest-cpp==2.3.0 tensorboard==2.13.0
-
-# Install Z3 optional dependency for Windows builds.
-python -m pip install z3-solver==4.12.2.0
-
 run_tests() {
     # Run nvidia-smi if available
     for path in '/c/Program Files/NVIDIA Corporation/NVSMI/nvidia-smi.exe' /c/Windows/System32/nvidia-smi.exe; do
@@ -49,7 +60,9 @@ run_tests() {
         fi
     done
 
-    if [[ $NUM_TEST_SHARDS -eq 1 ]]; then
+    if [[ "${TEST_CONFIG}" == *functorch* ]]; then
+        "$SCRIPT_HELPERS_DIR"/install_test_functorch.bat
+    elif [[ $NUM_TEST_SHARDS -eq 1 ]]; then
         "$SCRIPT_HELPERS_DIR"/test_python_shard.bat
         "$SCRIPT_HELPERS_DIR"/test_custom_script_ops.bat
         "$SCRIPT_HELPERS_DIR"/test_custom_backend.bat

.circleci/README.md

@@ -1,4 +1,468 @@
 Warning
 =======
 
-PyTorch migration from CircleCI to github actions has been completed. All continuous integration & deployment workflows are defined in  `.github/workflows` folder
+Contents may be out of date. Our CircleCI workflows are gradually being migrated to Github actions.
+
+Structure of CI
+===============
+
+setup job:
+1. Does a git checkout
+2. Persists CircleCI scripts (everything in `.circleci`) into a workspace.  Why?
+   We don't always do a Git checkout on all subjobs, but we usually
+   still want to be able to call scripts one way or another in a subjob.
+   Persisting files this way lets us have access to them without doing a
+   checkout.  This workspace is conventionally mounted on `~/workspace`
+   (this is distinguished from `~/project`, which is the conventional
+   working directory that CircleCI will default to starting your jobs
+   in.)
+3. Write out the commit message to `.circleci/COMMIT_MSG`.  This is so
+   we can determine in subjobs if we should actually run the jobs or
+   not, even if there isn't a Git checkout.
+
+
+CircleCI configuration generator
+================================
+
+One may no longer make changes to the `.circleci/config.yml` file directly.
+Instead, one must edit these Python scripts or files in the `verbatim-sources/` directory.
+
+
+Usage
+----------
+
+1. Make changes to these scripts.
+2. Run the `regenerate.sh` script in this directory and commit the script changes and the resulting change to `config.yml`.
+
+You'll see a build failure on GitHub if the scripts don't agree with the checked-in version.
+
+
+Motivation
+----------
+
+These scripts establish a single, authoritative source of documentation for the CircleCI configuration matrix.
+The documentation, in the form of diagrams, is automatically generated and cannot drift out of sync with the YAML content.
+
+Furthermore, consistency is enforced within the YAML config itself, by using a single source of data to generate
+multiple parts of the file.
+
+* Facilitates one-off culling/enabling of CI configs for testing PRs on special targets
+
+Also see https://github.com/pytorch/pytorch/issues/17038
+
+
+Future direction
+----------------
+
+### Declaring sparse config subsets
+See comment [here](https://github.com/pytorch/pytorch/pull/17323#pullrequestreview-206945747):
+
+In contrast with a full recursive tree traversal of configuration dimensions,
+> in the future I think we actually want to decrease our matrix somewhat and have only a few mostly-orthogonal builds that taste as many different features as possible on PRs, plus a more complete suite on every PR and maybe an almost full suite nightly/weekly (we don't have this yet). Specifying PR jobs in the future might be easier to read with an explicit list when we come to this.
+----------------
+----------------
+
+# How do the binaries / nightlies / releases work?
+
+### What is a binary?
+
+A binary or package (used interchangeably) is a pre-built collection of c++ libraries, header files, python bits, and other files. We build these and distribute them so that users do not need to install from source.
+
+A **binary configuration** is a collection of
+
+* release or nightly
+    * releases are stable, nightlies are beta and built every night
+* python version
+    * linux: 3.7m (mu is wide unicode or something like that. It usually doesn't matter but you should know that it exists)
+    * macos: 3.7, 3.8
+    * windows: 3.7, 3.8
+* cpu version
+    * cpu, cuda 9.0, cuda 10.0
+    * The supported cuda versions occasionally change
+* operating system
+    * Linux - these are all built on CentOS. There haven't been any problems in the past building on CentOS and using on Ubuntu
+    * MacOS
+    * Windows - these are built on Azure pipelines
+* devtoolset version (gcc compiler version)
+    * This only matters on Linux cause only Linux uses gcc. tldr is gcc made a backwards incompatible change from gcc 4.8 to gcc 5, because it had to change how it implemented std::vector and std::string
+
+### Where are the binaries?
+
+The binaries are built in CircleCI. There are nightly binaries built every night at 9pm PST (midnight EST) and release binaries corresponding to Pytorch releases, usually every few months.
+
+We have 3 types of binary packages
+
+* pip packages - nightlies are stored on s3 (pip install -f \<a s3 url\>). releases are stored in a pip repo (pip install torch) (ask Soumith about this)
+* conda packages - nightlies and releases are both stored in a conda repo. Nighty packages have a '_nightly' suffix
+* libtorch packages - these are zips of all the c++ libraries, header files, and sometimes dependencies. These are c++ only
+    * shared with dependencies (the only supported option for Windows)
+    * static with dependencies
+    * shared without dependencies
+    * static without dependencies
+
+All binaries are built in CircleCI workflows except Windows. There are checked-in workflows (committed into the .circleci/config.yml) to build the nightlies every night. Releases are built by manually pushing a PR that builds the suite of release binaries (overwrite the config.yml to build the release)
+
+# CircleCI structure of the binaries
+
+Some quick vocab:
+
+* A \**workflow** is a CircleCI concept; it is a DAG of '**jobs**'. ctrl-f 'workflows' on https://github.com/pytorch/pytorch/blob/master/.circleci/config.yml to see the workflows.
+* **jobs** are a sequence of '**steps**'
+* **steps** are usually just a bash script or a builtin CircleCI command. *All steps run in new environments, environment variables declared in one script DO NOT persist to following steps*
+* CircleCI has a **workspace**, which is essentially a cache between steps of the *same job* in which you can store artifacts between steps.
+
+## How are the workflows structured?
+
+The nightly binaries have 3 workflows. We have one job (actually 3 jobs:  build, test, and upload) per binary configuration
+
+1. binary_builds
+    1. every day midnight EST
+    2. linux: https://github.com/pytorch/pytorch/blob/master/.circleci/verbatim-sources/linux-binary-build-defaults.yml
+    3. macos: https://github.com/pytorch/pytorch/blob/master/.circleci/verbatim-sources/macos-binary-build-defaults.yml
+    4. For each binary configuration, e.g. linux_conda_3.7_cpu there is a
+        1. binary_linux_conda_3.7_cpu_build
+            1. Builds the build. On linux jobs this uses the 'docker executor'.
+            2. Persists the package to the workspace
+        2. binary_linux_conda_3.7_cpu_test
+            1. Loads the package to the workspace
+            2. Spins up a docker image (on Linux), mapping the package and code repos into the docker
+            3. Runs some smoke tests in the docker
+            4. (Actually, for macos this is a step rather than a separate job)
+        3. binary_linux_conda_3.7_cpu_upload
+            1. Logs in to aws/conda
+            2. Uploads the package
+2. update_s3_htmls
+    1. every day 5am EST
+    2. https://github.com/pytorch/pytorch/blob/master/.circleci/verbatim-sources/binary_update_htmls.yml
+    3. See below for what these are for and why they're needed
+    4. Three jobs that each examine the current contents of aws and the conda repo and update some html files in s3
+3. binarysmoketests
+    1. every day
+    2. https://github.com/pytorch/pytorch/blob/master/.circleci/verbatim-sources/nightly-build-smoke-tests-defaults.yml
+    3. For each binary configuration, e.g. linux_conda_3.7_cpu there is a
+        1. smoke_linux_conda_3.7_cpu
+            1. Downloads the package from the cloud, e.g. using the official pip or conda instructions
+            2. Runs the smoke tests
+
+## How are the jobs structured?
+
+The jobs are in https://github.com/pytorch/pytorch/tree/master/.circleci/verbatim-sources. Jobs are made of multiple steps. There are some shared steps used by all the binaries/smokes. Steps of these jobs are all delegated to scripts in https://github.com/pytorch/pytorch/tree/master/.circleci/scripts .
+
+* Linux jobs: https://github.com/pytorch/pytorch/blob/master/.circleci/verbatim-sources/linux-binary-build-defaults.yml
+    * binary_linux_build.sh
+    * binary_linux_test.sh
+    * binary_linux_upload.sh
+* MacOS jobs: https://github.com/pytorch/pytorch/blob/master/.circleci/verbatim-sources/macos-binary-build-defaults.yml
+    * binary_macos_build.sh
+    * binary_macos_test.sh
+    * binary_macos_upload.sh
+* Update html jobs: https://github.com/pytorch/pytorch/blob/master/.circleci/verbatim-sources/binary_update_htmls.yml
+    * These delegate from the pytorch/builder repo
+    * https://github.com/pytorch/builder/blob/master/cron/update_s3_htmls.sh
+    * https://github.com/pytorch/builder/blob/master/cron/upload_binary_sizes.sh
+* Smoke jobs (both linux and macos): https://github.com/pytorch/pytorch/blob/master/.circleci/verbatim-sources/nightly-build-smoke-tests-defaults.yml
+    * These delegate from the pytorch/builder repo
+    * https://github.com/pytorch/builder/blob/master/run_tests.sh
+    * https://github.com/pytorch/builder/blob/master/smoke_test.sh
+    * https://github.com/pytorch/builder/blob/master/check_binary.sh
+* Common shared code (shared across linux and macos): https://github.com/pytorch/pytorch/blob/master/.circleci/verbatim-sources/nightly-binary-build-defaults.yml
+    * binary_checkout.sh - checks out pytorch/builder repo. Right now this also checks out pytorch/pytorch, but it shouldn't. pytorch/pytorch should just be shared through the workspace. This can handle being run before binary_populate_env.sh
+    * binary_populate_env.sh - parses BUILD_ENVIRONMENT into the separate env variables that make up a binary configuration. Also sets lots of default values, the date, the version strings, the location of folders in s3, all sorts of things. This generally has to be run before other steps.
+    * binary_install_miniconda.sh - Installs miniconda, cross platform. Also hacks this for the update_binary_sizes job that doesn't have the right env variables
+    * binary_run_in_docker.sh - Takes a bash script file (the actual test code) from a hardcoded location, spins up a docker image, and runs the script inside the docker image
+
+### **Why do the steps all refer to scripts?**
+
+CircleCI creates a  final yaml file by inlining every <<* segment, so if we were to keep all the code in the config.yml itself then the config size would go over 4 MB and cause infra problems.
+
+### **What is binary_run_in_docker for?**
+
+So, CircleCI has several executor types: macos, machine, and docker are the ones we use. The 'machine' executor gives you two cores on some linux vm. The 'docker' executor gives you considerably more cores (nproc was 32 instead of 2 back when I tried in February). Since the dockers are faster, we try to run everything that we can in dockers. Thus
+
+* linux build jobs use the docker executor. Running them on the docker executor was at least 2x faster than running them on the machine executor
+* linux test jobs use the machine executor in order for them to properly interface with GPUs since docker executors cannot execute with attached GPUs
+* linux upload jobs use the machine executor. The upload jobs are so short that it doesn't really matter what they use
+* linux smoke test jobs use the machine executor for the same reason as the linux test jobs
+
+binary_run_in_docker.sh is a way to share the docker start-up code between the binary test jobs and the binary smoke test jobs
+
+### **Why does binary_checkout also checkout pytorch? Why shouldn't it?**
+
+We want all the nightly binary jobs to run on the exact same git commit, so we wrote our own checkout logic to ensure that the same commit was always picked. Later circleci changed that to use a single pytorch checkout and persist it through the workspace (they did this because our config file was too big, so they wanted to take a lot of the setup code into scripts, but the scripts needed the code repo to exist to be called, so they added a prereq step called 'setup' to checkout the code and persist the needed scripts to the workspace). The changes to the binary jobs were not properly tested, so they all broke from missing pytorch code no longer existing. We hotfixed the problem by adding the pytorch checkout back to binary_checkout, so now there's two checkouts of pytorch on the binary jobs. This problem still needs to be fixed, but it takes careful tracing of which code is being called where.
+
+# Code structure of the binaries (circleci agnostic)
+
+## Overview
+
+The code that runs the binaries lives in two places, in the normal [github.com/pytorch/pytorch](http://github.com/pytorch/pytorch), but also in [github.com/pytorch/builder](http://github.com/pytorch/builder), which is a repo that defines how all the binaries are built. The relevant code is
+
+
+```
+# All code needed to set-up environments for build code to run in,
+# but only code that is specific to the current CI system
+pytorch/pytorch
+- .circleci/                # Folder that holds all circleci related stuff
+  - config.yml              # GENERATED file that actually controls all circleci behavior
+  - verbatim-sources        # Used to generate job/workflow sections in ^
+  - scripts/                # Code needed to prepare circleci environments for binary build scripts
+- setup.py                  # Builds pytorch. This is wrapped in pytorch/builder
+- cmake files               # used in normal building of pytorch
+# All code needed to prepare a binary build, given an environment
+# with all the right variables/packages/paths.
+pytorch/builder
+# Given an installed binary and a proper python env, runs some checks
+# to make sure the binary was built the proper way. Checks things like
+# the library dependencies, symbols present, etc.
+- check_binary.sh
+# Given an installed binary, runs python tests to make sure everything
+# is in order. These should be de-duped. Right now they both run smoke
+# tests, but are called from different places. Usually just call some
+# import statements, but also has overlap with check_binary.sh above
+- run_tests.sh
+- smoke_test.sh
+# Folders that govern how packages are built. See paragraphs below
+- conda/
+  - build_pytorch.sh          # Entrypoint. Delegates to proper conda build folder
+  - switch_cuda_version.sh    # Switches activate CUDA installation in Docker
+  - pytorch-nightly/          # Build-folder
+- manywheel/
+  - build_cpu.sh              # Entrypoint for cpu builds
+  - build.sh                  # Entrypoint for CUDA builds
+  - build_common.sh           # Actual build script that ^^ call into
+- wheel/
+  - build_wheel.sh            # Entrypoint for wheel builds
+- windows/
+  - build_pytorch.bat         # Entrypoint for wheel builds on Windows
+```
+
+Every type of package has an entrypoint build script that handles the all the important logic.
+
+## Conda
+
+Linux, MacOS and Windows use the same code flow for the conda builds.
+
+Conda packages are built with conda-build, see https://conda.io/projects/conda-build/en/latest/resources/commands/conda-build.html
+
+Basically, you pass `conda build` a build folder (pytorch-nightly/ above) that contains a build script and a meta.yaml. The meta.yaml specifies in what python environment to build the package in, and what dependencies the resulting package should have, and the build script gets called in the env to build the thing.
+tl;dr on conda-build is
+
+1. Creates a brand new conda environment, based off of deps in the meta.yaml
+    1. Note that environment variables do not get passed into this build env unless they are specified in the meta.yaml
+    2. If the build fails this environment will stick around. You can activate it for much easier debugging. The “General Python” section below explains what exactly a python “environment” is.
+2. Calls build.sh in the environment
+3. Copies the finished package to a new conda env, also specified by the meta.yaml
+4. Runs some simple import tests (if specified in the meta.yaml)
+5. Saves the finished package as a tarball
+
+The build.sh we use is essentially a wrapper around `python setup.py build`, but it also manually copies in some of our dependent libraries into the resulting tarball and messes with some rpaths.
+
+The entrypoint file `builder/conda/build_conda.sh` is complicated because
+
+* It works for Linux, MacOS and Windows
+    * The mac builds used to create their own environments, since they all used to be on the same machine. There’s now a lot of extra logic to handle conda envs. This extra machinery could be removed
+* It used to handle testing too, which adds more logic messing with python environments too. This extra machinery could be removed.
+
+## Manywheels (linux pip and libtorch packages)
+
+Manywheels are pip packages for linux distros. Note that these manywheels are not actually manylinux compliant.
+
+`builder/manywheel/build_cpu.sh` and `builder/manywheel/build.sh` (for CUDA builds) just set different env vars and then call into `builder/manywheel/build_common.sh`
+
+The entrypoint file `builder/manywheel/build_common.sh` is really really complicated because
+
+* This used to handle building for several different python versions at the same time. The loops have been removed, but there's still unnecessary folders and movements here and there.
+    * The script is never used this way anymore. This extra machinery could be removed.
+* This used to handle testing the pip packages too. This is why there’s testing code at the end that messes with python installations and stuff
+    * The script is never used this way anymore. This extra machinery could be removed.
+* This also builds libtorch packages
+    * This should really be separate. libtorch packages are c++ only and have no python. They should not share infra with all the python specific stuff in this file.
+* There is a lot of messing with rpaths. This is necessary, but could be made much much simpler if the above issues were fixed.
+
+## Wheels (MacOS pip and libtorch packages)
+
+The entrypoint file `builder/wheel/build_wheel.sh` is complicated because
+
+* The mac builds used to all run on one machine (we didn’t have autoscaling mac machines till circleci). So this script handled siloing itself by setting-up and tearing-down its build env and siloing itself into its own build directory.
+    * The script is never used this way anymore. This extra machinery could be removed.
+* This also builds libtorch packages
+    * Ditto the comment above. This should definitely be separated out.
+
+Note that the MacOS Python wheels are still built in conda environments. Some of the dependencies present during build also come from conda.
+
+## Windows Wheels (Windows pip and libtorch packages)
+
+The entrypoint file `builder/windows/build_pytorch.bat` is complicated because
+
+* This used to handle building for several different python versions at the same time. This is why there are loops everywhere
+    * The script is never used this way anymore. This extra machinery could be removed.
+* This used to handle testing the pip packages too. This is why there’s testing code at the end that messes with python installations and stuff
+    * The script is never used this way anymore. This extra machinery could be removed.
+* This also builds libtorch packages
+    * This should really be separate. libtorch packages are c++ only and have no python. They should not share infra with all the python specific stuff in this file.
+
+Note that the Windows Python wheels are still built in conda environments. Some of the dependencies present during build also come from conda.
+
+## General notes
+
+### Note on run_tests.sh, smoke_test.sh, and check_binary.sh
+
+* These should all be consolidated
+* These must run on all OS types: MacOS, Linux, and Windows
+* These all run smoke tests at the moment. They inspect the packages some, maybe run a few import statements. They DO NOT run the python tests nor the cpp tests. The idea is that python tests on master and PR merges will catch all breakages. All these tests have to do is make sure the special binary machinery didn’t mess anything up.
+* There are separate run_tests.sh and smoke_test.sh because one used to be called by the smoke jobs and one used to be called by the binary test jobs (see circleci structure section above). This is still true actually, but these could be united into a single script that runs these checks, given an installed pytorch package.
+
+### Note on libtorch
+
+Libtorch packages are built in the wheel build scripts: manywheel/build_*.sh for linux and build_wheel.sh for mac. There are several things wrong with this
+
+* It’s confusing. Most of those scripts deal with python specifics.
+* The extra conditionals everywhere severely complicate the wheel build scripts
+* The process for building libtorch is different from the official instructions (a plain call to cmake, or a call to a script)
+
+### Note on docker images / Dockerfiles
+
+All linux builds occur in docker images. The docker images are
+
+* pytorch/conda-cuda
+    * Has ALL CUDA versions installed. The script pytorch/builder/conda/switch_cuda_version.sh sets /usr/local/cuda to a symlink to e.g. /usr/local/cuda-10.0 to enable different CUDA builds
+    * Also used for cpu builds
+* pytorch/manylinux-cuda90
+* pytorch/manylinux-cuda100
+    * Also used for cpu builds
+
+The Dockerfiles are available in pytorch/builder, but there is no circleci job or script to build these docker images, and they cannot be run locally (unless you have the correct local packages/paths). Only Soumith can build them right now.
+
+### General Python
+
+* This is still a good explanation of python installations https://caffe2.ai/docs/faq.html#why-do-i-get-import-errors-in-python-when-i-try-to-use-caffe2
+
+# How to manually rebuild the binaries
+
+tl;dr make a PR that looks like https://github.com/pytorch/pytorch/pull/21159
+
+Sometimes we want to push a change to master and then rebuild all of today's binaries after that change. As of May 30, 2019 there isn't a way to manually run a workflow in the UI. You can manually re-run a workflow, but it will use the exact same git commits as the first run and will not include any changes. So we have to make a PR and then force circleci to run the binary workflow instead of the normal tests. The above PR is an example of how to do this; essentially you copy-paste the binarybuilds workflow steps into the default workflow steps. If you need to point the builder repo to a different commit then you'd need to change https://github.com/pytorch/pytorch/blob/master/.circleci/scripts/binary_checkout.sh#L42-L45 to checkout what you want.
+
+## How to test changes to the binaries via .circleci
+
+Writing PRs that test the binaries is annoying, since the default circleci jobs that run on PRs are not the jobs that you want to run. Likely, changes to the binaries will touch something under .circleci/ and require that .circleci/config.yml be regenerated (.circleci/config.yml controls all .circleci behavior, and is generated using `.circleci/regenerate.sh` in python 3.7). But you also need to manually hardcode the binary jobs that you want to test into the .circleci/config.yml workflow, so you should actually make at least two commits, one for your changes and one to temporarily hardcode jobs. See https://github.com/pytorch/pytorch/pull/22928 as an example of how to do this.
+
+```sh
+# Make your changes
+touch .circleci/verbatim-sources/nightly-binary-build-defaults.yml
+# Regenerate the yaml, has to be in python 3.7
+.circleci/regenerate.sh
+# Make a commit
+git add .circleci *
+git commit -m "My real changes"
+git push origin my_branch
+# Now hardcode the jobs that you want in the .circleci/config.yml workflows section
+# Also eliminate ensure-consistency and should_run_job checks
+# e.g. https://github.com/pytorch/pytorch/commit/2b3344bfed8772fe86e5210cc4ee915dee42b32d
+# Make a commit you won't keep
+git add .circleci
+git commit -m "[DO NOT LAND] testing binaries for above changes"
+git push origin my_branch
+# Now you need to make some changes to the first commit.
+git rebase -i HEAD~2 # mark the first commit as 'edit'
+# Make the changes
+touch .circleci/verbatim-sources/nightly-binary-build-defaults.yml
+.circleci/regenerate.sh
+# Ammend the commit and recontinue
+git add .circleci
+git commit --amend
+git rebase --continue
+# Update the PR, need to force since the commits are different now
+git push origin my_branch --force
+```
+
+The advantage of this flow is that you can make new changes to the base commit and regenerate the .circleci without having to re-write which binary jobs you want to test on. The downside is that all updates will be force pushes.
+
+## How to build a binary locally
+
+### Linux
+
+You can build Linux binaries locally easily using docker.
+
+```sh
+# Run the docker
+# Use the correct docker image, pytorch/conda-cuda used here as an example
+#
+# -v path/to/foo:path/to/bar makes path/to/foo on your local machine (the
+#    machine that you're running the command on) accessible to the docker
+#    container at path/to/bar. So if you then run `touch path/to/bar/baz`
+#    in the docker container then you will see path/to/foo/baz on your local
+#    machine. You could also clone the pytorch and builder repos in the docker.
+#
+# If you know how, add ccache as a volume too and speed up everything
+docker run \
+    -v your/pytorch/repo:/pytorch \
+    -v your/builder/repo:/builder \
+    -v where/you/want/packages/to/appear:/final_pkgs \
+    -it pytorch/conda-cuda /bin/bash
+# Export whatever variables are important to you. All variables that you'd
+# possibly need are in .circleci/scripts/binary_populate_env.sh
+# You should probably always export at least these 3 variables
+export PACKAGE_TYPE=conda
+export DESIRED_PYTHON=3.7
+export DESIRED_CUDA=cpu
+# Call the entrypoint
+# `|& tee foo.log` just copies all stdout and stderr output to foo.log
+# The builds generate lots of output so you probably need this when
+# building locally.
+/builder/conda/build_pytorch.sh |& tee build_output.log
+```
+
+**Building CUDA binaries on docker**
+
+You can build CUDA binaries on CPU only machines, but you can only run CUDA binaries on CUDA machines. This means that you can build a CUDA binary on a docker on your laptop if you so choose (though it’s gonna take a long time).
+
+For Facebook employees, ask about beefy machines that have docker support and use those instead of your laptop; it will be 5x as fast.
+
+### MacOS
+
+There’s no easy way to generate reproducible hermetic MacOS environments. If you have a Mac laptop then you can try emulating the .circleci environments as much as possible, but you probably have packages in /usr/local/, possibly installed by brew, that will probably interfere with the build. If you’re trying to repro an error on a Mac build in .circleci and you can’t seem to repro locally, then my best advice is actually to iterate on .circleci    :/
+
+But if you want to try, then I’d recommend
+
+```sh
+# Create a new terminal
+# Clear your LD_LIBRARY_PATH and trim as much out of your PATH as you
+# know how to do
+# Install a new miniconda
+# First remove any other python or conda installation from your PATH
+# Always install miniconda 3, even if building for Python <3
+new_conda="~/my_new_conda"
+conda_sh="$new_conda/install_miniconda.sh"
+curl -o "$conda_sh" https://repo.anaconda.com/miniconda/Miniconda3-latest-MacOSX-x86_64.sh
+chmod +x "$conda_sh"
+"$conda_sh" -b -p "$MINICONDA_ROOT"
+rm -f "$conda_sh"
+export PATH="~/my_new_conda/bin:$PATH"
+# Create a clean python env
+# All MacOS builds use conda to manage the python env and dependencies
+# that are built with, even the pip packages
+conda create -yn binary python=2.7
+conda activate binary
+# Export whatever variables are important to you. All variables that you'd
+# possibly need are in .circleci/scripts/binary_populate_env.sh
+# You should probably always export at least these 3 variables
+export PACKAGE_TYPE=conda
+export DESIRED_PYTHON=3.7
+export DESIRED_CUDA=cpu
+# Call the entrypoint you want
+path/to/builder/wheel/build_wheel.sh
+```
+
+N.B. installing a brand new miniconda is important. This has to do with how conda installations work. See the “General Python” section above, but tldr; is that
+
+1. You make the ‘conda’ command accessible by prepending `path/to/conda_root/bin` to your PATH.
+2. You make a new env and activate it, which then also gets prepended to your PATH. Now you have `path/to/conda_root/envs/new_env/bin:path/to/conda_root/bin:$PATH`
+3. Now say you (or some code that you ran) call python executable `foo`
+    1. if you installed `foo` in `new_env`, then `path/to/conda_root/envs/new_env/bin/foo` will get called, as expected.
+    2. But if you forgot to installed `foo` in `new_env` but happened to previously install it in your root conda env (called ‘base’), then unix/linux will still find `path/to/conda_root/bin/foo` . This is dangerous, since `foo` can be a different version than you want; `foo` can even be for an incompatible python version!
+
+Newer conda versions and proper python hygiene can prevent this, but just install a new miniconda to be safe.
+
+### Windows
+
+TODO: fill in

.circleci/cimodel/data/binary_build_data.py

@@ -0,0 +1,171 @@
+"""
+This module models the tree of configuration variants
+for "smoketest" builds.
+
+Each subclass of ConfigNode represents a layer of the configuration hierarchy.
+These tree nodes encapsulate the logic for whether a branch of the hierarchy
+should be "pruned".
+"""
+
+from collections import OrderedDict
+
+from cimodel.lib.conf_tree import ConfigNode
+import cimodel.data.dimensions as dimensions
+
+
+LINKING_DIMENSIONS = [
+    "shared",
+    "static",
+]
+
+
+DEPS_INCLUSION_DIMENSIONS = [
+    "with-deps",
+    "without-deps",
+]
+
+
+def get_processor_arch_name(gpu_version):
+    return "cpu" if not gpu_version else (
+        "cu" + gpu_version.strip("cuda") if gpu_version.startswith("cuda") else gpu_version
+    )
+
+CONFIG_TREE_DATA = OrderedDict(
+)
+
+# GCC config variants:
+#
+# All the nightlies (except libtorch with new gcc ABI) are built with devtoolset7,
+# which can only build with old gcc ABI. It is better than devtoolset3
+# because it understands avx512, which is needed for good fbgemm performance.
+#
+# Libtorch with new gcc ABI is built with gcc 5.4 on Ubuntu 16.04.
+LINUX_GCC_CONFIG_VARIANTS = OrderedDict(
+    manywheel=['devtoolset7'],
+    conda=['devtoolset7'],
+    libtorch=[
+        "devtoolset7",
+        "gcc5.4_cxx11-abi",
+    ],
+)
+
+WINDOWS_LIBTORCH_CONFIG_VARIANTS = [
+    "debug",
+    "release",
+]
+
+
+class TopLevelNode(ConfigNode):
+    def __init__(self, node_name, config_tree_data, smoke):
+        super().__init__(None, node_name)
+
+        self.config_tree_data = config_tree_data
+        self.props["smoke"] = smoke
+
+    def get_children(self):
+        return [OSConfigNode(self, x, c, p) for (x, (c, p)) in self.config_tree_data.items()]
+
+
+class OSConfigNode(ConfigNode):
+    def __init__(self, parent, os_name, gpu_versions, py_tree):
+        super().__init__(parent, os_name)
+
+        self.py_tree = py_tree
+        self.props["os_name"] = os_name
+        self.props["gpu_versions"] = gpu_versions
+
+    def get_children(self):
+        return [PackageFormatConfigNode(self, k, v) for k, v in self.py_tree.items()]
+
+
+class PackageFormatConfigNode(ConfigNode):
+    def __init__(self, parent, package_format, python_versions):
+        super().__init__(parent, package_format)
+
+        self.props["python_versions"] = python_versions
+        self.props["package_format"] = package_format
+
+
+    def get_children(self):
+        if self.find_prop("os_name") == "linux":
+            return [LinuxGccConfigNode(self, v) for v in LINUX_GCC_CONFIG_VARIANTS[self.find_prop("package_format")]]
+        elif self.find_prop("os_name") == "windows" and self.find_prop("package_format") == "libtorch":
+            return [WindowsLibtorchConfigNode(self, v) for v in WINDOWS_LIBTORCH_CONFIG_VARIANTS]
+        else:
+            return [ArchConfigNode(self, v) for v in self.find_prop("gpu_versions")]
+
+
+class LinuxGccConfigNode(ConfigNode):
+    def __init__(self, parent, gcc_config_variant):
+        super().__init__(parent, "GCC_CONFIG_VARIANT=" + str(gcc_config_variant))
+
+        self.props["gcc_config_variant"] = gcc_config_variant
+
+    def get_children(self):
+        gpu_versions = self.find_prop("gpu_versions")
+
+        # XXX devtoolset7 on CUDA 9.0 is temporarily disabled
+        # see https://github.com/pytorch/pytorch/issues/20066
+        if self.find_prop("gcc_config_variant") == 'devtoolset7':
+            gpu_versions = filter(lambda x: x != "cuda_90", gpu_versions)
+
+        # XXX disabling conda rocm build since docker images are not there
+        if self.find_prop("package_format") == 'conda':
+            gpu_versions = filter(lambda x: x not in dimensions.ROCM_VERSION_LABELS, gpu_versions)
+
+        # XXX libtorch rocm build  is temporarily disabled
+        if self.find_prop("package_format") == 'libtorch':
+            gpu_versions = filter(lambda x: x not in dimensions.ROCM_VERSION_LABELS, gpu_versions)
+
+        return [ArchConfigNode(self, v) for v in gpu_versions]
+
+
+class WindowsLibtorchConfigNode(ConfigNode):
+    def __init__(self, parent, libtorch_config_variant):
+        super().__init__(parent, "LIBTORCH_CONFIG_VARIANT=" + str(libtorch_config_variant))
+
+        self.props["libtorch_config_variant"] = libtorch_config_variant
+
+    def get_children(self):
+        return [ArchConfigNode(self, v) for v in self.find_prop("gpu_versions")]
+
+
+class ArchConfigNode(ConfigNode):
+    def __init__(self, parent, gpu):
+        super().__init__(parent, get_processor_arch_name(gpu))
+
+        self.props["gpu"] = gpu
+
+    def get_children(self):
+        return [PyVersionConfigNode(self, v) for v in self.find_prop("python_versions")]
+
+
+class PyVersionConfigNode(ConfigNode):
+    def __init__(self, parent, pyver):
+        super().__init__(parent, pyver)
+
+        self.props["pyver"] = pyver
+
+    def get_children(self):
+        package_format = self.find_prop("package_format")
+        os_name = self.find_prop("os_name")
+
+        has_libtorch_variants = package_format == "libtorch" and os_name == "linux"
+        linking_variants = LINKING_DIMENSIONS if has_libtorch_variants else []
+
+        return [LinkingVariantConfigNode(self, v) for v in linking_variants]
+
+
+class LinkingVariantConfigNode(ConfigNode):
+    def __init__(self, parent, linking_variant):
+        super().__init__(parent, linking_variant)
+
+    def get_children(self):
+        return [DependencyInclusionConfigNode(self, v) for v in DEPS_INCLUSION_DIMENSIONS]
+
+
+class DependencyInclusionConfigNode(ConfigNode):
+    def __init__(self, parent, deps_variant):
+        super().__init__(parent, deps_variant)
+
+        self.props["libtorch_variant"] = "-".join([self.parent.get_label(), self.get_label()])

.circleci/cimodel/data/binary_build_definitions.py

@@ -0,0 +1,243 @@
+from collections import OrderedDict
+
+import cimodel.data.simple.util.branch_filters as branch_filters
+import cimodel.data.binary_build_data as binary_build_data
+import cimodel.lib.conf_tree as conf_tree
+import cimodel.lib.miniutils as miniutils
+
+class Conf(object):
+    def __init__(self, os, gpu_version, pydistro, parms, smoke, libtorch_variant, gcc_config_variant, libtorch_config_variant):
+
+        self.os = os
+        self.gpu_version = gpu_version
+        self.pydistro = pydistro
+        self.parms = parms
+        self.smoke = smoke
+        self.libtorch_variant = libtorch_variant
+        self.gcc_config_variant = gcc_config_variant
+        self.libtorch_config_variant = libtorch_config_variant
+
+    def gen_build_env_parms(self):
+        elems = [self.pydistro] + self.parms + [binary_build_data.get_processor_arch_name(self.gpu_version)]
+        if self.gcc_config_variant is not None:
+            elems.append(str(self.gcc_config_variant))
+        if self.libtorch_config_variant is not None:
+            elems.append(str(self.libtorch_config_variant))
+        return elems
+
+    def gen_docker_image(self):
+        if self.gcc_config_variant == 'gcc5.4_cxx11-abi':
+            if self.gpu_version is None:
+                return miniutils.quote("pytorch/libtorch-cxx11-builder:cpu")
+            else:
+                return miniutils.quote(
+                    f"pytorch/libtorch-cxx11-builder:{self.gpu_version}"
+                )
+        if self.pydistro == "conda":
+            if self.gpu_version is None:
+                return miniutils.quote("pytorch/conda-builder:cpu")
+            else:
+                return miniutils.quote(
+                    f"pytorch/conda-builder:{self.gpu_version}"
+                )
+
+        docker_word_substitution = {
+            "manywheel": "manylinux",
+            "libtorch": "manylinux",
+        }
+
+        docker_distro_prefix = miniutils.override(self.pydistro, docker_word_substitution)
+
+        # The cpu nightlies are built on the pytorch/manylinux-cuda102 docker image
+        # TODO cuda images should consolidate into tag-base images similar to rocm
+        alt_docker_suffix = "cuda102" if not self.gpu_version else (
+            "rocm:" + self.gpu_version.strip("rocm") if self.gpu_version.startswith("rocm") else self.gpu_version)
+        docker_distro_suffix = alt_docker_suffix if self.pydistro != "conda" else (
+            "cuda" if alt_docker_suffix.startswith("cuda") else "rocm")
+        return miniutils.quote("pytorch/" + docker_distro_prefix + "-" + docker_distro_suffix)
+
+    def get_name_prefix(self):
+        return "smoke" if self.smoke else "binary"
+
+    def gen_build_name(self, build_or_test, nightly):
+
+        parts = [self.get_name_prefix(), self.os] + self.gen_build_env_parms()
+
+        if nightly:
+            parts.append("nightly")
+
+        if self.libtorch_variant:
+            parts.append(self.libtorch_variant)
+
+        if not self.smoke:
+            parts.append(build_or_test)
+
+        joined = "_".join(parts)
+        return joined.replace(".", "_")
+
+    def gen_workflow_job(self, phase, upload_phase_dependency=None, nightly=False):
+        job_def = OrderedDict()
+        job_def["name"] = self.gen_build_name(phase, nightly)
+        job_def["build_environment"] = miniutils.quote(" ".join(self.gen_build_env_parms()))
+        if self.smoke:
+            job_def["requires"] = [
+                "update_s3_htmls",
+            ]
+            job_def["filters"] = branch_filters.gen_filter_dict(
+                branches_list=["postnightly"],
+            )
+        else:
+            filter_branch = r"/.*/"
+            job_def["filters"] = branch_filters.gen_filter_dict(
+                branches_list=[filter_branch],
+                tags_list=[branch_filters.RC_PATTERN],
+            )
+        if self.libtorch_variant:
+            job_def["libtorch_variant"] = miniutils.quote(self.libtorch_variant)
+        if phase == "test":
+            if not self.smoke:
+                job_def["requires"] = [self.gen_build_name("build", nightly)]
+            if not (self.smoke and self.os == "macos") and self.os != "windows":
+                job_def["docker_image"] = self.gen_docker_image()
+
+            # fix this. only works on cuda not rocm
+            if self.os != "windows" and self.gpu_version:
+                job_def["use_cuda_docker_runtime"] = miniutils.quote("1")
+        else:
+            if self.os == "linux" and phase != "upload":
+                job_def["docker_image"] = self.gen_docker_image()
+
+        if phase == "test":
+            if self.gpu_version:
+                if self.os == "windows":
+                    job_def["executor"] = "windows-with-nvidia-gpu"
+                else:
+                    job_def["resource_class"] = "gpu.medium"
+
+        os_name = miniutils.override(self.os, {"macos": "mac"})
+        job_name = "_".join([self.get_name_prefix(), os_name, phase])
+        return {job_name : job_def}
+
+    def gen_upload_job(self, phase, requires_dependency):
+        """Generate binary_upload job for configuration
+
+        Output looks similar to:
+
+      - binary_upload:
+          name: binary_linux_manywheel_3_7m_cu113_devtoolset7_nightly_upload
+          context: org-member
+          requires: binary_linux_manywheel_3_7m_cu113_devtoolset7_nightly_test
+          filters:
+            branches:
+              only:
+                - nightly
+            tags:
+              only: /v[0-9]+(\\.[0-9]+)*-rc[0-9]+/
+          package_type: manywheel
+          upload_subfolder: cu113
+        """
+        return {
+            "binary_upload": OrderedDict({
+                "name": self.gen_build_name(phase, nightly=True),
+                "context": "org-member",
+                "requires": [self.gen_build_name(
+                    requires_dependency,
+                    nightly=True
+                )],
+                "filters": branch_filters.gen_filter_dict(
+                    branches_list=["nightly"],
+                    tags_list=[branch_filters.RC_PATTERN],
+                ),
+                "package_type": self.pydistro,
+                "upload_subfolder": binary_build_data.get_processor_arch_name(
+                    self.gpu_version,
+                ),
+            })
+        }
+
+def get_root(smoke, name):
+
+    return binary_build_data.TopLevelNode(
+        name,
+        binary_build_data.CONFIG_TREE_DATA,
+        smoke,
+    )
+
+
+def gen_build_env_list(smoke):
+
+    root = get_root(smoke, "N/A")
+    config_list = conf_tree.dfs(root)
+
+    newlist = []
+    for c in config_list:
+        conf = Conf(
+            c.find_prop("os_name"),
+            c.find_prop("gpu"),
+            c.find_prop("package_format"),
+            [c.find_prop("pyver")],
+            c.find_prop("smoke") and not (c.find_prop("os_name") == "macos_arm64"),  # don't test arm64
+            c.find_prop("libtorch_variant"),
+            c.find_prop("gcc_config_variant"),
+            c.find_prop("libtorch_config_variant"),
+        )
+        newlist.append(conf)
+
+    return newlist
+
+def predicate_exclude_macos(config):
+    return config.os == "linux" or config.os == "windows"
+
+def get_nightly_uploads():
+    configs = gen_build_env_list(False)
+    mylist = []
+    for conf in configs:
+        phase_dependency = "test" if predicate_exclude_macos(conf) else "build"
+        mylist.append(conf.gen_upload_job("upload", phase_dependency))
+
+    return mylist
+
+def get_post_upload_jobs():
+    return [
+        {
+            "update_s3_htmls": {
+                "name": "update_s3_htmls",
+                "context": "org-member",
+                "filters": branch_filters.gen_filter_dict(
+                    branches_list=["postnightly"],
+                ),
+            },
+        },
+    ]
+
+def get_nightly_tests():
+
+    configs = gen_build_env_list(False)
+    filtered_configs = filter(predicate_exclude_macos, configs)
+
+    tests = []
+    for conf_options in filtered_configs:
+        yaml_item = conf_options.gen_workflow_job("test", nightly=True)
+        tests.append(yaml_item)
+
+    return tests
+
+
+def get_jobs(toplevel_key, smoke):
+    jobs_list = []
+    configs = gen_build_env_list(smoke)
+    phase = "build" if toplevel_key == "binarybuilds" else "test"
+    for build_config in configs:
+        # don't test for macos_arm64 as it's cross compiled
+        if phase != "test" or build_config.os != "macos_arm64":
+            jobs_list.append(build_config.gen_workflow_job(phase, nightly=True))
+
+    return jobs_list
+
+
+def get_binary_build_jobs():
+    return get_jobs("binarybuilds", False)
+
+
+def get_binary_smoke_test_jobs():
+    return get_jobs("binarysmoketests", True)

.circleci/cimodel/data/dimensions.py

@@ -0,0 +1,24 @@
+PHASES = ["build", "test"]
+
+CUDA_VERSIONS = [
+    "102",
+    "113",
+    "116",
+    "117",
+]
+
+ROCM_VERSIONS = [
+    "4.3.1",
+    "4.5.2",
+]
+
+ROCM_VERSION_LABELS = ["rocm" + v for v in ROCM_VERSIONS]
+
+GPU_VERSIONS = [None] + ["cuda" + v for v in CUDA_VERSIONS] + ROCM_VERSION_LABELS
+
+STANDARD_PYTHON_VERSIONS = [
+    "3.7",
+    "3.8",
+    "3.9",
+    "3.10"
+]

.circleci/cimodel/data/pytorch_build_data.py

@@ -0,0 +1,289 @@
+from cimodel.lib.conf_tree import ConfigNode
+
+
+CONFIG_TREE_DATA = [
+]
+
+
+def get_major_pyver(dotted_version):
+    parts = dotted_version.split(".")
+    return "py" + parts[0]
+
+
+class TreeConfigNode(ConfigNode):
+    def __init__(self, parent, node_name, subtree):
+        super().__init__(parent, self.modify_label(node_name))
+        self.subtree = subtree
+        self.init2(node_name)
+
+    def modify_label(self, label):
+        return label
+
+    def init2(self, node_name):
+        pass
+
+    def get_children(self):
+        return [self.child_constructor()(self, k, v) for (k, v) in self.subtree]
+
+
+class TopLevelNode(TreeConfigNode):
+    def __init__(self, node_name, subtree):
+        super().__init__(None, node_name, subtree)
+
+    # noinspection PyMethodMayBeStatic
+    def child_constructor(self):
+        return DistroConfigNode
+
+
+class DistroConfigNode(TreeConfigNode):
+    def init2(self, node_name):
+        self.props["distro_name"] = node_name
+
+    def child_constructor(self):
+        distro = self.find_prop("distro_name")
+
+        next_nodes = {
+            "xenial": XenialCompilerConfigNode,
+            "bionic": BionicCompilerConfigNode,
+        }
+        return next_nodes[distro]
+
+
+class PyVerConfigNode(TreeConfigNode):
+    def init2(self, node_name):
+        self.props["pyver"] = node_name
+        self.props["abbreviated_pyver"] = get_major_pyver(node_name)
+        if node_name == "3.9":
+            self.props["abbreviated_pyver"] = "py3.9"
+
+    # noinspection PyMethodMayBeStatic
+    def child_constructor(self):
+        return ExperimentalFeatureConfigNode
+
+
+class ExperimentalFeatureConfigNode(TreeConfigNode):
+    def init2(self, node_name):
+        self.props["experimental_feature"] = node_name
+
+    def child_constructor(self):
+        experimental_feature = self.find_prop("experimental_feature")
+
+        next_nodes = {
+            "asan": AsanConfigNode,
+            "xla": XlaConfigNode,
+            "mps": MPSConfigNode,
+            "vulkan": VulkanConfigNode,
+            "parallel_tbb": ParallelTBBConfigNode,
+            "crossref": CrossRefConfigNode,
+            "dynamo": DynamoConfigNode,
+            "parallel_native": ParallelNativeConfigNode,
+            "onnx": ONNXConfigNode,
+            "libtorch": LibTorchConfigNode,
+            "important": ImportantConfigNode,
+            "build_only": BuildOnlyConfigNode,
+            "shard_test": ShardTestConfigNode,
+            "cuda_gcc_override": CudaGccOverrideConfigNode,
+            "pure_torch": PureTorchConfigNode,
+            "slow_gradcheck": SlowGradcheckConfigNode,
+        }
+        return next_nodes[experimental_feature]
+
+
+class SlowGradcheckConfigNode(TreeConfigNode):
+    def init2(self, node_name):
+        self.props["is_slow_gradcheck"] = True
+
+    def child_constructor(self):
+        return ExperimentalFeatureConfigNode
+
+class PureTorchConfigNode(TreeConfigNode):
+    def modify_label(self, label):
+        return "PURE_TORCH=" + str(label)
+
+    def init2(self, node_name):
+        self.props["is_pure_torch"] = node_name
+
+    def child_constructor(self):
+        return ImportantConfigNode
+
+
+class XlaConfigNode(TreeConfigNode):
+    def modify_label(self, label):
+        return "XLA=" + str(label)
+
+    def init2(self, node_name):
+        self.props["is_xla"] = node_name
+
+    def child_constructor(self):
+        return ImportantConfigNode
+
+class MPSConfigNode(TreeConfigNode):
+    def modify_label(self, label):
+        return "MPS=" + str(label)
+
+    def init2(self, node_name):
+        self.props["is_mps"] = node_name
+
+    def child_constructor(self):
+        return ImportantConfigNode
+
+
+class AsanConfigNode(TreeConfigNode):
+    def modify_label(self, label):
+        return "Asan=" + str(label)
+
+    def init2(self, node_name):
+        self.props["is_asan"] = node_name
+
+    def child_constructor(self):
+        return ExperimentalFeatureConfigNode
+
+
+class ONNXConfigNode(TreeConfigNode):
+    def modify_label(self, label):
+        return "Onnx=" + str(label)
+
+    def init2(self, node_name):
+        self.props["is_onnx"] = node_name
+
+    def child_constructor(self):
+        return ImportantConfigNode
+
+
+class VulkanConfigNode(TreeConfigNode):
+    def modify_label(self, label):
+        return "Vulkan=" + str(label)
+
+    def init2(self, node_name):
+        self.props["is_vulkan"] = node_name
+
+    def child_constructor(self):
+        return ImportantConfigNode
+
+
+class ParallelTBBConfigNode(TreeConfigNode):
+    def modify_label(self, label):
+        return "PARALLELTBB=" + str(label)
+
+    def init2(self, node_name):
+        self.props["parallel_backend"] = "paralleltbb"
+
+    def child_constructor(self):
+        return ImportantConfigNode
+
+
+class CrossRefConfigNode(TreeConfigNode):
+    def init2(self, node_name):
+        self.props["is_crossref"] = node_name
+
+    def child_constructor(self):
+        return ImportantConfigNode
+
+
+class DynamoConfigNode(TreeConfigNode):
+    def init2(self, node_name):
+        self.props["is_dynamo"] = node_name
+
+    def child_constructor(self):
+        return ImportantConfigNode
+
+
+class ParallelNativeConfigNode(TreeConfigNode):
+    def modify_label(self, label):
+        return "PARALLELNATIVE=" + str(label)
+
+    def init2(self, node_name):
+        self.props["parallel_backend"] = "parallelnative"
+
+    def child_constructor(self):
+        return ImportantConfigNode
+
+
+class LibTorchConfigNode(TreeConfigNode):
+    def modify_label(self, label):
+        return "BUILD_TEST_LIBTORCH=" + str(label)
+
+    def init2(self, node_name):
+        self.props["is_libtorch"] = node_name
+
+    def child_constructor(self):
+        return ExperimentalFeatureConfigNode
+
+
+class CudaGccOverrideConfigNode(TreeConfigNode):
+    def init2(self, node_name):
+        self.props["cuda_gcc_override"] = node_name
+
+    def child_constructor(self):
+        return ExperimentalFeatureConfigNode
+
+
+class BuildOnlyConfigNode(TreeConfigNode):
+    def init2(self, node_name):
+        self.props["build_only"] = node_name
+
+    def child_constructor(self):
+        return ExperimentalFeatureConfigNode
+
+
+class ShardTestConfigNode(TreeConfigNode):
+    def init2(self, node_name):
+        self.props["shard_test"] = node_name
+
+    def child_constructor(self):
+        return ImportantConfigNode
+
+
+class ImportantConfigNode(TreeConfigNode):
+    def modify_label(self, label):
+        return "IMPORTANT=" + str(label)
+
+    def init2(self, node_name):
+        self.props["is_important"] = node_name
+
+    def get_children(self):
+        return []
+
+
+class XenialCompilerConfigNode(TreeConfigNode):
+    def modify_label(self, label):
+        return label or "<unspecified>"
+
+    def init2(self, node_name):
+        self.props["compiler_name"] = node_name
+
+    # noinspection PyMethodMayBeStatic
+    def child_constructor(self):
+
+        return XenialCompilerVersionConfigNode if self.props["compiler_name"] else PyVerConfigNode
+
+
+class BionicCompilerConfigNode(TreeConfigNode):
+    def modify_label(self, label):
+        return label or "<unspecified>"
+
+    def init2(self, node_name):
+        self.props["compiler_name"] = node_name
+
+    # noinspection PyMethodMayBeStatic
+    def child_constructor(self):
+
+        return BionicCompilerVersionConfigNode if self.props["compiler_name"] else PyVerConfigNode
+
+
+class XenialCompilerVersionConfigNode(TreeConfigNode):
+    def init2(self, node_name):
+        self.props["compiler_version"] = node_name
+
+    # noinspection PyMethodMayBeStatic
+    def child_constructor(self):
+        return PyVerConfigNode
+
+
+class BionicCompilerVersionConfigNode(TreeConfigNode):
+    def init2(self, node_name):
+        self.props["compiler_version"] = node_name
+
+    # noinspection PyMethodMayBeStatic
+    def child_constructor(self):
+        return PyVerConfigNode

.circleci/cimodel/data/pytorch_build_definitions.py

@@ -0,0 +1,384 @@
+from collections import OrderedDict
+from dataclasses import dataclass, field
+from typing import List, Optional
+
+import cimodel.data.dimensions as dimensions
+import cimodel.lib.conf_tree as conf_tree
+import cimodel.lib.miniutils as miniutils
+from cimodel.data.pytorch_build_data import CONFIG_TREE_DATA, TopLevelNode
+from cimodel.data.simple.util.branch_filters import gen_filter_dict, RC_PATTERN
+from cimodel.data.simple.util.docker_constants import gen_docker_image
+
+
+@dataclass
+class Conf:
+    distro: str
+    parms: List[str]
+    parms_list_ignored_for_docker_image: Optional[List[str]] = None
+    pyver: Optional[str] = None
+    cuda_version: Optional[str] = None
+    rocm_version: Optional[str] = None
+    # TODO expand this to cover all the USE_* that we want to test for
+    #  tesnrorrt, leveldb, lmdb, redis, opencv, mkldnn, ideep, etc.
+    # (from https://github.com/pytorch/pytorch/pull/17323#discussion_r259453608)
+    is_xla: bool = False
+    is_vulkan: bool = False
+    is_pure_torch: bool = False
+    restrict_phases: Optional[List[str]] = None
+    gpu_resource: Optional[str] = None
+    dependent_tests: List = field(default_factory=list)
+    parent_build: Optional["Conf"] = None
+    is_libtorch: bool = False
+    is_important: bool = False
+    parallel_backend: Optional[str] = None
+    build_only: bool = False
+
+    @staticmethod
+    def is_test_phase(phase):
+        return "test" in phase
+
+    # TODO: Eliminate the special casing for docker paths
+    # In the short term, we *will* need to support special casing as docker images are merged for caffe2 and pytorch
+    def get_parms(self, for_docker):
+        leading = []
+        # We just don't run non-important jobs on pull requests;
+        # previously we also named them in a way to make it obvious
+        # if self.is_important and not for_docker:
+        #    leading.append("AAA")
+        leading.append("pytorch")
+        if self.is_xla and not for_docker:
+            leading.append("xla")
+        if self.is_vulkan and not for_docker:
+            leading.append("vulkan")
+        if self.is_libtorch and not for_docker:
+            leading.append("libtorch")
+        if self.is_pure_torch and not for_docker:
+            leading.append("pure_torch")
+        if self.parallel_backend is not None and not for_docker:
+            leading.append(self.parallel_backend)
+
+        cuda_parms = []
+        if self.cuda_version:
+            cudnn = "cudnn8" if self.cuda_version.startswith("11.") else "cudnn7"
+            cuda_parms.extend(["cuda" + self.cuda_version, cudnn])
+        if self.rocm_version:
+            cuda_parms.extend([f"rocm{self.rocm_version}"])
+        result = leading + ["linux", self.distro] + cuda_parms + self.parms
+        if not for_docker and self.parms_list_ignored_for_docker_image is not None:
+            result = result + self.parms_list_ignored_for_docker_image
+        return result
+
+    def gen_docker_image_path(self):
+        parms_source = self.parent_build or self
+        base_build_env_name = "-".join(parms_source.get_parms(True))
+        image_name, _ = gen_docker_image(base_build_env_name)
+        return miniutils.quote(image_name)
+
+    def gen_docker_image_requires(self):
+        parms_source = self.parent_build or self
+        base_build_env_name = "-".join(parms_source.get_parms(True))
+        _, requires = gen_docker_image(base_build_env_name)
+        return miniutils.quote(requires)
+
+    def get_build_job_name_pieces(self, build_or_test):
+        return self.get_parms(False) + [build_or_test]
+
+    def gen_build_name(self, build_or_test):
+        return (
+            ("_".join(map(str, self.get_build_job_name_pieces(build_or_test))))
+            .replace(".", "_")
+            .replace("-", "_")
+        )
+
+    def get_dependents(self):
+        return self.dependent_tests or []
+
+    def gen_workflow_params(self, phase):
+        parameters = OrderedDict()
+        build_job_name_pieces = self.get_build_job_name_pieces(phase)
+
+        build_env_name = "-".join(map(str, build_job_name_pieces))
+        parameters["build_environment"] = miniutils.quote(build_env_name)
+        parameters["docker_image"] = self.gen_docker_image_path()
+        if Conf.is_test_phase(phase) and self.gpu_resource:
+            parameters["use_cuda_docker_runtime"] = miniutils.quote("1")
+        if Conf.is_test_phase(phase):
+            resource_class = "large"
+            if self.gpu_resource:
+                resource_class = "gpu." + self.gpu_resource
+            if self.rocm_version is not None:
+                resource_class = "pytorch/amd-gpu"
+            parameters["resource_class"] = resource_class
+        if phase == "build" and self.rocm_version is not None:
+            parameters["resource_class"] = "xlarge"
+        if hasattr(self, 'filters'):
+            parameters['filters'] = self.filters
+        if self.build_only:
+            parameters['build_only'] = miniutils.quote(str(int(True)))
+        return parameters
+
+    def gen_workflow_job(self, phase):
+        job_def = OrderedDict()
+        job_def["name"] = self.gen_build_name(phase)
+
+        if Conf.is_test_phase(phase):
+
+            # TODO When merging the caffe2 and pytorch jobs, it might be convenient for a while to make a
+            #  caffe2 test job dependent on a pytorch build job. This way we could quickly dedup the repeated
+            #  build of pytorch in the caffe2 build job, and just run the caffe2 tests off of a completed
+            #  pytorch build job (from https://github.com/pytorch/pytorch/pull/17323#discussion_r259452641)
+
+            dependency_build = self.parent_build or self
+            job_def["requires"] = [dependency_build.gen_build_name("build")]
+            job_name = "pytorch_linux_test"
+        else:
+            job_name = "pytorch_linux_build"
+            job_def["requires"] = [self.gen_docker_image_requires()]
+
+        if not self.is_important:
+            job_def["filters"] = gen_filter_dict()
+        job_def.update(self.gen_workflow_params(phase))
+
+        return {job_name: job_def}
+
+
+# TODO This is a hack to special case some configs just for the workflow list
+class HiddenConf(object):
+    def __init__(self, name, parent_build=None, filters=None):
+        self.name = name
+        self.parent_build = parent_build
+        self.filters = filters
+
+    def gen_workflow_job(self, phase):
+        return {
+            self.gen_build_name(phase): {
+                "requires": [self.parent_build.gen_build_name("build")],
+                "filters": self.filters,
+            }
+        }
+
+    def gen_build_name(self, _):
+        return self.name
+
+class DocPushConf(object):
+    def __init__(self, name, parent_build=None, branch="master"):
+        self.name = name
+        self.parent_build = parent_build
+        self.branch = branch
+
+    def gen_workflow_job(self, phase):
+        return {
+            "pytorch_doc_push": {
+                "name": self.name,
+                "branch": self.branch,
+                "requires": [self.parent_build],
+                "context": "org-member",
+                "filters": gen_filter_dict(branches_list=["nightly"],
+                                           tags_list=RC_PATTERN)
+            }
+        }
+
+def gen_docs_configs(xenial_parent_config):
+    configs = []
+
+    configs.append(
+        HiddenConf(
+            "pytorch_python_doc_build",
+            parent_build=xenial_parent_config,
+            filters=gen_filter_dict(branches_list=["master", "main", "nightly"],
+                                    tags_list=RC_PATTERN),
+        )
+    )
+    configs.append(
+        DocPushConf(
+            "pytorch_python_doc_push",
+            parent_build="pytorch_python_doc_build",
+            branch="site",
+        )
+    )
+
+    configs.append(
+        HiddenConf(
+            "pytorch_cpp_doc_build",
+            parent_build=xenial_parent_config,
+            filters=gen_filter_dict(branches_list=["master", "main", "nightly"],
+                                    tags_list=RC_PATTERN),
+        )
+    )
+    configs.append(
+        DocPushConf(
+            "pytorch_cpp_doc_push",
+            parent_build="pytorch_cpp_doc_build",
+            branch="master",
+        )
+    )
+    return configs
+
+
+def get_root():
+    return TopLevelNode("PyTorch Builds", CONFIG_TREE_DATA)
+
+
+def gen_tree():
+    root = get_root()
+    configs_list = conf_tree.dfs(root)
+    return configs_list
+
+
+def instantiate_configs(only_slow_gradcheck):
+
+    config_list = []
+
+    root = get_root()
+    found_configs = conf_tree.dfs(root)
+    for fc in found_configs:
+
+        restrict_phases = None
+        distro_name = fc.find_prop("distro_name")
+        compiler_name = fc.find_prop("compiler_name")
+        compiler_version = fc.find_prop("compiler_version")
+        is_xla = fc.find_prop("is_xla") or False
+        is_asan = fc.find_prop("is_asan") or False
+        is_crossref = fc.find_prop("is_crossref") or False
+        is_dynamo = fc.find_prop("is_dynamo") or False
+        is_onnx = fc.find_prop("is_onnx") or False
+        is_pure_torch = fc.find_prop("is_pure_torch") or False
+        is_vulkan = fc.find_prop("is_vulkan") or False
+        is_slow_gradcheck = fc.find_prop("is_slow_gradcheck") or False
+        parms_list_ignored_for_docker_image = []
+
+        if only_slow_gradcheck ^ is_slow_gradcheck:
+            continue
+
+        python_version = None
+        if compiler_name == "cuda" or compiler_name == "android":
+            python_version = fc.find_prop("pyver")
+            parms_list = [fc.find_prop("abbreviated_pyver")]
+        else:
+            parms_list = ["py" + fc.find_prop("pyver")]
+
+        cuda_version = None
+        rocm_version = None
+        if compiler_name == "cuda":
+            cuda_version = fc.find_prop("compiler_version")
+
+        elif compiler_name == "rocm":
+            rocm_version = fc.find_prop("compiler_version")
+            restrict_phases = ["build", "test1", "test2", "caffe2_test"]
+
+        elif compiler_name == "android":
+            android_ndk_version = fc.find_prop("compiler_version")
+            # TODO: do we need clang to compile host binaries like protoc?
+            parms_list.append("clang5")
+            parms_list.append("android-ndk-" + android_ndk_version)
+            android_abi = fc.find_prop("android_abi")
+            parms_list_ignored_for_docker_image.append(android_abi)
+            restrict_phases = ["build"]
+
+        elif compiler_name:
+            gcc_version = compiler_name + (fc.find_prop("compiler_version") or "")
+            parms_list.append(gcc_version)
+
+        if is_asan:
+            parms_list.append("asan")
+            python_version = fc.find_prop("pyver")
+            parms_list[0] = fc.find_prop("abbreviated_pyver")
+
+        if is_crossref:
+            parms_list_ignored_for_docker_image.append("crossref")
+
+        if is_dynamo:
+            parms_list_ignored_for_docker_image.append("dynamo")
+
+        if is_onnx:
+            parms_list.append("onnx")
+            python_version = fc.find_prop("pyver")
+            parms_list[0] = fc.find_prop("abbreviated_pyver")
+            restrict_phases = ["build", "ort_test1", "ort_test2"]
+
+        if cuda_version:
+            cuda_gcc_version = fc.find_prop("cuda_gcc_override") or "gcc7"
+            parms_list.append(cuda_gcc_version)
+
+        is_libtorch = fc.find_prop("is_libtorch") or False
+        is_important = fc.find_prop("is_important") or False
+        parallel_backend = fc.find_prop("parallel_backend") or None
+        build_only = fc.find_prop("build_only") or False
+        shard_test = fc.find_prop("shard_test") or False
+        # TODO: fix pure_torch python test packaging issue.
+        if shard_test:
+            restrict_phases = ["build"] if restrict_phases is None else restrict_phases
+            restrict_phases.extend(["test1", "test2"])
+        if build_only or is_pure_torch:
+            restrict_phases = ["build"]
+
+        if is_slow_gradcheck:
+            parms_list_ignored_for_docker_image.append("old")
+            parms_list_ignored_for_docker_image.append("gradcheck")
+
+        gpu_resource = None
+        if cuda_version and cuda_version != "10":
+            gpu_resource = "medium"
+
+        c = Conf(
+            distro_name,
+            parms_list,
+            parms_list_ignored_for_docker_image,
+            python_version,
+            cuda_version,
+            rocm_version,
+            is_xla,
+            is_vulkan,
+            is_pure_torch,
+            restrict_phases,
+            gpu_resource,
+            is_libtorch=is_libtorch,
+            is_important=is_important,
+            parallel_backend=parallel_backend,
+            build_only=build_only,
+        )
+
+        # run docs builds on "pytorch-linux-xenial-py3.7-gcc5.4". Docs builds
+        # should run on a CPU-only build that runs on all PRs.
+        # XXX should this be updated to a more modern build?
+        if (
+            distro_name == "xenial"
+            and fc.find_prop("pyver") == "3.7"
+            and cuda_version is None
+            and parallel_backend is None
+            and not is_vulkan
+            and not is_pure_torch
+            and compiler_name == "gcc"
+            and fc.find_prop("compiler_version") == "5.4"
+        ):
+            c.filters = gen_filter_dict(branches_list=r"/.*/",
+                                        tags_list=RC_PATTERN)
+            c.dependent_tests = gen_docs_configs(c)
+
+        config_list.append(c)
+
+    return config_list
+
+
+def get_workflow_jobs(only_slow_gradcheck=False):
+
+    config_list = instantiate_configs(only_slow_gradcheck)
+
+    x = []
+    for conf_options in config_list:
+
+        phases = conf_options.restrict_phases or dimensions.PHASES
+
+        for phase in phases:
+
+            # TODO why does this not have a test?
+            if Conf.is_test_phase(phase) and conf_options.cuda_version == "10":
+                continue
+
+            x.append(conf_options.gen_workflow_job(phase))
+
+        # TODO convert to recursion
+        for conf in conf_options.get_dependents():
+            x.append(conf.gen_workflow_job("test"))
+
+    return x

.circleci/cimodel/data/simple/anaconda_prune_defintions.py

@@ -0,0 +1,28 @@
+from collections import OrderedDict
+
+from cimodel.data.simple.util.branch_filters import gen_filter_dict
+from cimodel.lib.miniutils import quote
+
+
+CHANNELS_TO_PRUNE = ["pytorch-nightly", "pytorch-test"]
+PACKAGES_TO_PRUNE = "pytorch torchvision torchaudio torchtext ignite torchcsprng"
+
+
+def gen_workflow_job(channel: str):
+    return OrderedDict(
+        {
+            "anaconda_prune": OrderedDict(
+                {
+                    "name": f"anaconda-prune-{channel}",
+                    "context": quote("org-member"),
+                    "packages": quote(PACKAGES_TO_PRUNE),
+                    "channel": channel,
+                    "filters": gen_filter_dict(branches_list=["postnightly"]),
+                }
+            )
+        }
+    )
+
+
+def get_workflow_jobs():
+    return [gen_workflow_job(channel) for channel in CHANNELS_TO_PRUNE]

.circleci/cimodel/data/simple/docker_definitions.py

@@ -0,0 +1,39 @@
+from collections import OrderedDict
+
+from cimodel.lib.miniutils import quote
+from cimodel.data.simple.util.branch_filters import gen_filter_dict, RC_PATTERN
+
+
+# NOTE: All hardcoded docker image builds have been migrated to GHA
+IMAGE_NAMES = [
+]
+
+# This entry should be an element from the list above
+# This should contain the image matching the "slow_gradcheck" entry in
+# pytorch_build_data.py
+SLOW_GRADCHECK_IMAGE_NAME = "pytorch-linux-xenial-cuda10.2-cudnn7-py3-gcc7"
+
+def get_workflow_jobs(images=IMAGE_NAMES, only_slow_gradcheck=False):
+    """Generates a list of docker image build definitions"""
+    ret = []
+    for image_name in images:
+        if image_name.startswith('docker-'):
+            image_name = image_name.lstrip('docker-')
+        if only_slow_gradcheck and image_name is not SLOW_GRADCHECK_IMAGE_NAME:
+            continue
+
+        parameters = OrderedDict({
+            "name": quote(f"docker-{image_name}"),
+            "image_name": quote(image_name),
+        })
+        if image_name == "pytorch-linux-xenial-py3.7-gcc5.4":
+            # pushing documentation on tags requires CircleCI to also
+            # build all the dependencies on tags, including this docker image
+            parameters['filters'] = gen_filter_dict(branches_list=r"/.*/",
+                                                    tags_list=RC_PATTERN)
+        ret.append(OrderedDict(
+            {
+                "docker_build_job": parameters
+            }
+        ))
+    return ret

.circleci/cimodel/data/simple/ios_definitions.py

@@ -0,0 +1,82 @@
+from cimodel.data.simple.util.versions import MultiPartVersion
+from cimodel.data.simple.util.branch_filters import gen_filter_dict_exclude
+import cimodel.lib.miniutils as miniutils
+
+XCODE_VERSION = MultiPartVersion([12, 5, 1])
+
+
+class ArchVariant:
+    def __init__(self, name, custom_build_name=""):
+        self.name = name
+        self.custom_build_name = custom_build_name
+
+    def render(self):
+        extra_parts = [self.custom_build_name] if len(self.custom_build_name) > 0 else []
+        return "-".join([self.name] + extra_parts).replace("_", "-")
+
+
+def get_platform(arch_variant_name):
+    return "SIMULATOR" if arch_variant_name == "x86_64" else "OS"
+
+
+class IOSJob:
+    def __init__(self, xcode_version, arch_variant, is_org_member_context=True, extra_props=None):
+        self.xcode_version = xcode_version
+        self.arch_variant = arch_variant
+        self.is_org_member_context = is_org_member_context
+        self.extra_props = extra_props
+
+    def gen_name_parts(self):
+        version_parts = self.xcode_version.render_dots_or_parts("-")
+        build_variant_suffix = self.arch_variant.render()
+        return [
+            "ios",
+        ] + version_parts + [
+            build_variant_suffix,
+        ]
+
+    def gen_job_name(self):
+        return "-".join(self.gen_name_parts())
+
+    def gen_tree(self):
+        platform_name = get_platform(self.arch_variant.name)
+        props_dict = {
+            "name": self.gen_job_name(),
+            "build_environment": self.gen_job_name(),
+            "ios_arch": self.arch_variant.name,
+            "ios_platform": platform_name,
+        }
+
+        if self.is_org_member_context:
+            props_dict["context"] = "org-member"
+
+        if self.extra_props:
+            props_dict.update(self.extra_props)
+
+        props_dict["filters"] = gen_filter_dict_exclude()
+
+        return [{"pytorch_ios_build": props_dict}]
+
+
+WORKFLOW_DATA = [
+    IOSJob(XCODE_VERSION, ArchVariant("x86_64"), is_org_member_context=False, extra_props={
+        "lite_interpreter": miniutils.quote(str(int(True)))}),
+    # IOSJob(XCODE_VERSION, ArchVariant("arm64"), extra_props={
+    #     "lite_interpreter": miniutils.quote(str(int(True)))}),
+    # IOSJob(XCODE_VERSION, ArchVariant("arm64", "metal"), extra_props={
+    #     "use_metal": miniutils.quote(str(int(True))),
+    #     "lite_interpreter": miniutils.quote(str(int(True)))}),
+    # IOSJob(XCODE_VERSION, ArchVariant("arm64", "custom-ops"), extra_props={
+    #     "op_list": "mobilenetv2.yaml",
+    #     "lite_interpreter": miniutils.quote(str(int(True)))}),
+    IOSJob(XCODE_VERSION, ArchVariant("x86_64", "coreml"), is_org_member_context=False, extra_props={
+        "use_coreml": miniutils.quote(str(int(True))),
+        "lite_interpreter": miniutils.quote(str(int(True)))}),
+    # IOSJob(XCODE_VERSION, ArchVariant("arm64", "coreml"), extra_props={
+    #     "use_coreml": miniutils.quote(str(int(True))),
+    #     "lite_interpreter": miniutils.quote(str(int(True)))}),
+]
+
+
+def get_workflow_jobs():
+    return [item.gen_tree() for item in WORKFLOW_DATA]

.circleci/cimodel/data/simple/macos_definitions.py

@@ -0,0 +1,54 @@
+class MacOsJob:
+    def __init__(self, os_version, is_build=False, is_test=False, extra_props=tuple()):
+        # extra_props is tuple type, because mutable data structures for argument defaults
+        # is not recommended.
+        self.os_version = os_version
+        self.is_build = is_build
+        self.is_test = is_test
+        self.extra_props = dict(extra_props)
+
+    def gen_tree(self):
+        non_phase_parts = ["pytorch", "macos", self.os_version, "py3"]
+
+        extra_name_list = [name for name, exist in self.extra_props.items() if exist]
+        full_job_name_list = (
+            non_phase_parts
+            + extra_name_list
+            + [
+                "build" if self.is_build else None,
+                "test" if self.is_test else None,
+            ]
+        )
+
+        full_job_name = "_".join(list(filter(None, full_job_name_list)))
+
+        test_build_dependency = "_".join(non_phase_parts + ["build"])
+        extra_dependencies = [test_build_dependency] if self.is_test else []
+        job_dependencies = extra_dependencies
+
+        # Yes we name the job after itself, it needs a non-empty value in here
+        # for the YAML output to work.
+        props_dict = {"requires": job_dependencies, "name": full_job_name}
+
+        return [{full_job_name: props_dict}]
+
+
+WORKFLOW_DATA = [
+    MacOsJob("10_15", is_build=True),
+    MacOsJob("10_13", is_build=True),
+    MacOsJob(
+        "10_13",
+        is_build=False,
+        is_test=True,
+    ),
+    MacOsJob(
+        "10_13",
+        is_build=True,
+        is_test=True,
+        extra_props=tuple({"lite_interpreter": True}.items()),
+    ),
+]
+
+
+def get_workflow_jobs():
+    return [item.gen_tree() for item in WORKFLOW_DATA]

.circleci/cimodel/data/simple/mobile_definitions.py

@@ -0,0 +1,53 @@
+"""
+PyTorch Mobile PR builds (use linux host toolchain + mobile build options)
+"""
+
+import cimodel.lib.miniutils as miniutils
+import cimodel.data.simple.util.branch_filters
+
+
+class MobileJob:
+    def __init__(
+            self,
+            docker_image,
+            docker_requires,
+            variant_parts,
+            is_master_only=False):
+        self.docker_image = docker_image
+        self.docker_requires = docker_requires
+        self.variant_parts = variant_parts
+        self.is_master_only = is_master_only
+
+    def gen_tree(self):
+        non_phase_parts = [
+            "pytorch",
+            "linux",
+            "xenial",
+            "py3",
+            "clang5",
+            "mobile",
+        ] + self.variant_parts
+
+        full_job_name = "_".join(non_phase_parts)
+        build_env_name = "-".join(non_phase_parts)
+
+        props_dict = {
+            "build_environment": build_env_name,
+            "build_only": miniutils.quote(str(int(True))),
+            "docker_image": self.docker_image,
+            "requires": self.docker_requires,
+            "name": full_job_name,
+        }
+
+        if self.is_master_only:
+            props_dict["filters"] = cimodel.data.simple.util.branch_filters.gen_filter_dict()
+
+        return [{"pytorch_linux_build": props_dict}]
+
+
+WORKFLOW_DATA = [
+]
+
+
+def get_workflow_jobs():
+    return [item.gen_tree() for item in WORKFLOW_DATA]

.circleci/cimodel/data/simple/nightly_ios.py

@@ -0,0 +1,85 @@
+import cimodel.data.simple.ios_definitions as ios_definitions
+import cimodel.lib.miniutils as miniutils
+
+
+class IOSNightlyJob:
+    def __init__(self,
+                 variant,
+                 is_full_jit=False,
+                 is_upload=False):
+
+        self.variant = variant
+        self.is_full_jit = is_full_jit
+        self.is_upload = is_upload
+
+    def get_phase_name(self):
+        return "upload" if self.is_upload else "build"
+
+    def get_common_name_pieces(self, sep):
+
+        extra_name_suffix = [self.get_phase_name()] if self.is_upload else []
+
+        extra_name = ["full_jit"] if self.is_full_jit else []
+
+        common_name_pieces = [
+            "ios",
+        ] + extra_name + [
+        ] + ios_definitions.XCODE_VERSION.render_dots_or_parts(sep) + [
+            "nightly",
+            self.variant,
+            "build",
+        ] + extra_name_suffix
+
+        return common_name_pieces
+
+    def gen_job_name(self):
+        return "_".join(["pytorch"] + self.get_common_name_pieces(None))
+
+    def gen_tree(self):
+        build_configs = BUILD_CONFIGS_FULL_JIT if self.is_full_jit else BUILD_CONFIGS
+        extra_requires = [x.gen_job_name() for x in build_configs] if self.is_upload else []
+
+        props_dict = {
+            "build_environment": "-".join(["libtorch"] + self.get_common_name_pieces(".")),
+            "requires": extra_requires,
+            "context": "org-member",
+            "filters": {"branches": {"only": "nightly"}},
+        }
+
+        if not self.is_upload:
+            props_dict["ios_arch"] = self.variant
+            props_dict["ios_platform"] = ios_definitions.get_platform(self.variant)
+            props_dict["name"] = self.gen_job_name()
+            props_dict["use_metal"] = miniutils.quote(str(int(True)))
+            props_dict["use_coreml"] = miniutils.quote(str(int(True)))
+
+        if self.is_full_jit:
+            props_dict["lite_interpreter"] = miniutils.quote(str(int(False)))
+
+        template_name = "_".join([
+            "binary",
+            "ios",
+            self.get_phase_name(),
+        ])
+
+        return [{template_name: props_dict}]
+
+
+BUILD_CONFIGS = [
+    IOSNightlyJob("x86_64"),
+    IOSNightlyJob("arm64"),
+]
+
+BUILD_CONFIGS_FULL_JIT = [
+    IOSNightlyJob("x86_64", is_full_jit=True),
+    IOSNightlyJob("arm64", is_full_jit=True),
+]
+
+WORKFLOW_DATA = BUILD_CONFIGS + BUILD_CONFIGS_FULL_JIT + [
+    IOSNightlyJob("binary", is_full_jit=False, is_upload=True),
+    IOSNightlyJob("binary", is_full_jit=True, is_upload=True),
+]
+
+
+def get_workflow_jobs():
+    return [item.gen_tree() for item in WORKFLOW_DATA]